December, 2018

CURRICULUM VITAE

SCOTT H. HOLAN

Professor of StatisticsDepartment of StatisticsUniversity of MissouriColumbia, MO 65211–6100(573) 882-4491 (Office); (573) 884-5524 (Fax)holans@missouri.eduhttp://www.stat.missouri.edu/∼holans

EDUCATION:

2004 Ph.D., Statistics, Texas A&M UniversityDissertation: “Time Series Exponential Models: Theory and Methods”Major Advisor: Dr. Emanuel Parzen

1999 M.S., Mathematics, University of Illinois at Chicago

1997 B.S., Mathematics, University of Illinois at Chicago(Highest Distinction in Mathematics)

EXPERIENCE:

2016– Senior Research Fellow, Office of the Associate Director for Research andMethodology (ADRM), U.S. Census Bureau.

2015– Professor of Statistics, Department of Statistics, University of Missouri.

2015– Professor of Public Affairs, Truman School of Public Affairs, University of Mis-souri.

2012–2015 Associate Professor of Public Affairs, Truman School of Public Affairs, Univer-sity of Missouri.

2011–2015 Associate Professor of Statistics, Department of Statistics, University of Mis-souri – (with tenure).

2005–2011 Assistant Professor of Statistics, Department of Statistics, University of Mis-souri.

June 2016 Visiting Researcher – U.S. Census Bureau - Center for Statistical Research andMethodology (CSRM), Washington, D.C. (Summer at Census Program).

July 2010 Visiting Researcher – U.S. Census Bureau - Statistical Research Division (SRD),Washington, D.C. (Summer at Census Program).

Fall 2009 Visiting Research Fellow – Statistical and Applied Mathematical Sciences In-stitute (SAMSI).

May 2009 Visiting Researcher – U.S. Census Bureau - Statistical Research Division (SRD),Washington, D.C. (Summer at Census Program).

2005–2007 Senior Research Fellow – Office of Survey Methods Research (OSMR), Bureau ofLabor Statistics, Washington, D.C. (In residence at BLS during the summer).

2004–2005 Visiting Assistant Professor, Department of Statistics, University of Missouri.

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HONORS AND AWARDS:

2017 Statistical Partnerships Among Academe, Industry, and Government (SPAIG)Award (National Science Foundation-Census Research Network; NCRN)

2017 Travel Award – NBER–NSF Time Series Conference

2016 H.O. Hartley Award (Department of Statistics – Texas A&M University;

Alumni award for distinguished service to the discipline of Statistics.)

2016 Travel Award – NBER–NSF Time Series Conference

2015 Travel Award – NBER–NSF Time Series Conference

2014 Elected Fellow of the American Statistical Association (ASA)

2014 Elected Member of the International Statistical Institute (ISI)

2014 Travel Award – Seminar on Bayesian Inference in Econometrics and Statistics(SBIES)

2013 Faculty International Travel Award, University of Missouri

2012-2014 National Academy of Science (CNSTAT) – Addressing Priority Technical Issuesfor the Next Decade of the American Community Survey (Panel Member)

2012 Travel Award – NBER–NSF Time Series Conference

2011 Travel Award – NBER–NSF Time Series Conference

2009–2011 NISS-NASS Cross-Sector Research Fellowship (Faculty Mentor Fellowship)

2009 National Academy of Science (CNSTAT) – Steering Committee for Reviewof Confidentiality Criteria for Survey of Earned Doctorate Data (CommitteeMember)

2009 Travel Award – Seminar on Bayesian Inference in Econometrics and Statistics(SBIES)

2008 Travel Award – Institute of Mathematical Statistics New Researchers Confer-ence

2006–2007 NISS New Researcher Fellowship

2006 Faculty International Travel Award, University of Missouri

2005 ASA/NSF/BLS Senior Research Fellowship

2004 Chapter Service Recognition Award, American Statistical Association

2003 Second Place, Best Contributed Poster Competition, Joint Statistical Meetings

2000 Dean’s Graduate Scholar, Texas A&M University

2000 Mobile Aggie Graduate Merit Fellow, Texas A&M University

1997 Phi Kappa Phi National Honor Society

FUNDED RESEARCH:

6/19–7/22 (Pending) Multi-Distributioin, Multivariate, and Multiscale Spatio-Temporal Mo-dels with Applications to Official Statistics, NSF-SES (MMS): Role: PI (Co-PI – C.K.Wikle; PI at FSU J.R. Bradley) MU Portion $789,646 – (50%).

12/18–11/21 (Recommended for Funding) Regional Phase Blockage and Left Censored Datain the Middle East, USAF AFMC AFRL/RVBYE: Role: Co-PI (PI – E. Sandvol) $561,319– (50%).

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1/17–8/17 Department of Statistics Research and Teaching Seed (SRTS) Grant Program:Graduate and Undergraduate Student Support for Computing and Research: Role: PI$14,050.

10/16–9/19 NCRN-MN: Improving the Interpretability and Usability of the American Com-munity Survey Through Hierarchical Multiscale Spatio-Temporal Statistical Models: Sup-plemental Award, NSF-SES: Role: PI (Co-PIs – N. Cressie and C.K. Wikle) $511,872 –(60%).

9/16–8/19 Senior Research Fellow, U.S. Census Bureau, Intergovernmental Personnel Act(IPA) with University of Missouri, September 1, 2016–August 31, 2019 (anticipated con-tinuation to August 31, 2020), $531,388.

6/16 Collaborative Research on Spatio-Temporal Modeling, Summer at Census Program, U.S.Census Bureau. Role: PI, $3,000

5/14–2/19 Molecular Analysis of Photoacoustically Captured Circlulating Melanoma Cells,NIH-R01 (PI – J. Viator, $1, 548, 373 awarded). Role: PI – MU Subcontract: $138,830

8/14–12/18 Statistical Methods for Agricultural Research, USDA-ARS, Role: PI $181,397

10/12–12/17 NCRN-MN: Improving the Interpretability and Usability of the American Com-munity Survey Through Hierarchical Multiscale Spatio-Temporal Statistical Models: Sup-plemental Award, NSF-SES: Role: PI (Co-PIs – N. Cressie and C.K. Wikle) $399,596 –(60%).

10/11–9/16 NCRN-MN: Improving the Interpretability and Usability of the American Com-munity Survey Through Hierarchical Multiscale Spatio-Temporal Statistical Models, NSF-SES: Role: PI (Co-PIs – N. Cressie and C.K. Wikle) $2,854,170 – (60%).

7/11–6/12 Hierarchical Polynomial Nonlinear Time-Frequency Functional Data Models, Uni-versity of Missouri Research Board, Role: PI $10,466 (Total Costs)

7/10 Collaborative Research on Seasonal Adjustment Methodology, Summer at Census Pro-gram, U.S. Census Bureau. Role: PI, $5,000

5/10–5/11 A 21st Century Program in Cancer Research: Targeting Metastatic Cancer Cells toImprove Diagnosis and Therapy, Mizzou Advantage, Role: Co–PI (PI – J. Viator) $70,000(Total Costs; $50, 000-Mizzou Advantage, $20, 000-Life Science Center) – (10%).

8/09–8/14 Development of Population and Survival Estimates for Pallid Sturgeon in theLower Missouri River, U.S. Geological Survey. Role: PI $457,201

6/09–5/11 Statistical Multi-Source Predictive Models and Error Estimates with Applicationto Major USDA Crop Production Forecasts and Estimates, NISS-NASS Cross Sector Re-search Fellowship. Role: Faculty Mentor, $38,000

5/09–5/11 Photoacoustic Detection of Circulating Melanoma Cells, NIH-R21. Role: Co–PI(PI – J. Viator) $351,996 (Total Costs) – (10%).

5/09 Collaborative Research on Seasonal Adjustment Methodology, Summer at Census Pro-gram, U.S. Census Bureau. Role: PI, $5,000

1/09–1/12 Photoacoustic Detection of Circulating Melanoma Cells in Blood, Missouri LifeSciences Trust Fund Research Board. Role: Co–PI (PI – J. Viator) $407,789 (Total Costs)– (10%).

6/07–6/08 Photoacoustic Detection of Circulating Breast Cancer Cells, ASLMS (AmericanSociety of Laser Med and Surgery). Role: Co–PI (PI – J. Viator) $33,063 (Total Costs)– (5%).

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8/06–8/08 Photoacoustic Detection of Circulating Melanoma Cells, Wallace H. Coulter Foun-dation. Role: Co–PI (PI – J. Viator) $232,582 (Total Costs) – (10%).

5/06–5/07 Multi-Scale Modelling and Mapping Coarse Woody Debris in the Eastern UnitedStates, United States Forest Service. Role: Co–PI (PI – Z. Fan) $47,500 (Total Costs) –(10%).

5/06–8/07 New Researcher Fellowship, National Institute of Statistical Science (NISS). Role:PI, $28,200

5/05–9/07 Diagnostics for Model Based Seasonal Adjustment, American Statistical Asso-ciation / National Science Foundation / Bureau of Labor Statistics (ASA/NSF/BLS) –Research Fellowship. Role: PI, $99,021

BOOKS:

Handbook of Discrete–Valued Time Series. Edited by Richard A. Davis, Scott H. Holan,Robert Lund, and Nalini Ravishanker. Chapman & Hall/CRC, 2016 (484 pp.).

Economic Time Series: Modeling and Seasonality. Edited by William R. Bell, Scott H.Holan, and Tucker S. McElroy. Chapman & Hall/CRC, 2012 (554 pp.).

REFEREED JOURNAL ARTICLES:

[1.] van Toor, M.L., Avril, A., Wu, G., Holan, S.H., and Waldstrom, J. (2018) As the DuckFlies – Estimating the Dispersal of Low-Pathogenic Avian Influenza Viruses by Migratingmallards. (To Appear – Frontiers in Ecology and Evolution, Behavioral and EvolutionaryEcology Section).

[2.] Veum, K.S., Parker, P.A., Sudduth, K.A., and Holan, S.H., (2018) Predicting Profile SoilProperties with Reflectance Spectra via Bayesian Covariate Assisted External Parameter Or-thogonalization (To Appear – Sensors).

[3.] Holan, S.H., and Ravishanker, N. (2018) Time Series Clustering and Classification via Fre-quency Domain Methods. (To Appear – WIREs).

[4.] Weinberg, D.H., Abowd, J.A., Belli, R.F., Cressie, N., Eck, A., Folch, D.C., Holan, S.H.,Levenstein, M.C., Olson, K.M., Reiter, J.P., Shapiro, M.D., Smyth, J., Soh, L-K., Spencer,B.D., Spielman, S.E., Wikle, C.K., Vilhuber, L. (2018) Effects of a Government-AcademicPartnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of theFederal Statistical System? (To Appear – Journal of Survey Statistics and Methodology).

[5.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2018) Hierarchical Models for Spatial Datawith Errors that are Correlated with the Latent Process. (To Appear – Statistica Sinica).

[6.] Quick, H., Holan, S.H., and Wikle, C.K. (2018) Generating Partially Synthetic GeocodedPublic Use Data with Decreased Disclosure Risk Using Differential Smoothing. Journal of theRoyal Statistical Society – Series A, 3: 649–661.

[7.] Bradley, J.R., Holan, S.H., and Wikle, C.K. (2018) Computationally Efficient Multivari-ate Spatio-Temporal Models for High-Dimensional Count-Valued Data (With Discussion).Bayesian Analysis, 1: 253–281.

[8.] Bradley, J.R., Holan, S.H., and Wikle, C.K. (2018) Rejoinder – Computationally EfficientMultivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data. BayesianAnalysis, 1: 302–310.

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[9.] Wu, G. and Holan, S.H. (2017) Bayesian Hierarchical Multi-Population Multistate Jolly-Seber Models with Covariates: Application to the Pallid Sturgeon Population AssessmentProgram, Journal of the American Statistical Association, 518: 471–483. (JASA Applicationsand Case Studies – Invited Paper: Joint Statistical Meetings 2018).

[10.] Simpson, M., Wikle, C.K., and Holan, S.H. (2017) Adaptively-Tuned Particle Swarm Opti-mization with Application to Spatial Design, Stat, 6: 145–159.

[11.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2017) Regionalization of Multiscale SpatialProcesses using a Criterion for Spatial Aggregation Error, Journal of the Royal StatisticalSociety – Series B, 79: 815–832.

[12.] Holan, S.H., McElroy, T.S., and Wu, G. (2017) The Cepstral Model for Multivariate TimeSeries: The Vector Exponential Model, Statistica Sinica, 27: 23–42.

[13.] Bradley, J.R., Holan, S.H., and Wikle, C.K. (2016) Multivariate Spatio-Temporal SurveyFusion with Application to the American Community Survey and Local Area UnemploymentStatistics, STAT, 5: 224–233.

[14.] McElroy, T.S. and Holan, S.H. (2016) Computation of the Autocovariances for Time Serieswith Multiple Long-Range Persistencies, Computational Statistics and Data Analysis, 101:44–56.

[15.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016) Bayesian Spatial Change of Support forCount-Valued Survey Data with Application to the American Community Survey. Journal ofthe American Statistical Association, 111: 472–487.

[16.] Yang, W.H., Holan, S.H., and Wikle, C.K. (2016) Bayesian Lattice Filters for Time-VaryingAutoregression and Time-Frequency Analysis. Bayesian Analysis, 11: 977–1003.

[17.] Bradley, J.R., Holan, S.H., and Wikle, C.K. (2015) Multivariate Spatio-Temporal Models forHigh-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynam-ics. Annals of Applied Statistics, 9: 1761–1791.

[18.] Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015) Bayesian Marked Point ProcessModeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography.Spatial Statistics, 14: 439–451.

[19.] Quick, H., Holan, S.H., and Wikle, C.K. (2015) Zeros and Ones: A Case for SuppressingZeros in Sensitive Count Data with an Application to Stroke Mortality, Stat, 4: 227–234.

[20.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015) Spatio-Temporal Change of Support withApplication to American Community Survey Multi-Year Period Estimates, Stat, 4: 255–270.

[21.] Ryan, M., Bradley, J.R., Oswald, T., Wikle, C.K., and Holan, S.H. (2015) An Analysis ofBullying and Suicide in the United States using a Non-Gaussian Multivariate Spatial Model.(Refereed Conference Proceedings), Proceedings of The National Conference On UndergraduateResearch (NCUR), 155–161.

[22.] Porter, A.T., Holan, S.H., and Wikle, C.K. (2015) Bayesian Semiparametric HierarchicalEmpirical Likelihood Spatial Models. Journal of Statistical Planning and Inference, 165: 78–90.

[23.] Porter, A.T., Holan, S.H., and Wikle, C.K. (2015) Multivariate Spatial Hierarchical BayesianEmpirical Likelihood Methods for Small Area Estimation. Stat, 4: 108–116.

[24.] Wu, G., Holan, S.H., Nilon, C.H., and Wikle, C.K. (2015) Bayesian Binomial Mixture Modelsfor Estimating Abundance in Ecological Monitoring Studies. Annals of Applied Statistics. 9:1–26.

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[25.] Porter, A.T., Wikle, C.K., and Holan, S.H. (2015) Small Area Estimation via MultivariateFay-Herriot Models With Latent Spatial Dependence. Australian & New Zealand Journal ofStatistics. 57: 15–29.

[26.] Yang, W.H., Wikle, C.K., Holan, S.H., Meyers, D.B., and Sudduth, K.A. (2015) BayesianAnalysis of Spatially-Dependent Functional Responses with Spatially-Dependent Multi-

Dimensional Functional Predictors. Statistica Sinica. 25: 205–223.

[27.] Porter, A.T., Holan, S.H., Wikle, C.K., and Cressie, N. (2014) Spatial Fay-Herriot Modelsfor Small Area Estimation With Functional Covariates. Spatial Statistics. 10: 27–42.

[28.] Wikle, C.K., Holan, S.H., Sudduth, K.A., and Meyers, D.B. (2014) Soil Property Estimationand Design for Agroecosystem Management Using Hierarchical Geospatial Functional DataModels. Journal of the Indian Society of Agricultural Statistics. 68: 203–216.

[29.] Gladish, D., Wikle, C.K., and Holan, S.H (2014) Covariate–Based Cepstral Parameterizationsfor Time–Varying Spatial Error Covariances. Environmetrics. 2: 69–83.

[30.] McElroy, T.S. and Holan, S.H. (2014) Asymptotic Theory of Cepstral Random Fields. Annalsof Statistics. 42: 64–86.

[31.] Wu, G., Holan, S.H., and Wikle, C.K. (2013) Hierarchical Bayesian Spatio-Temporal

Conway-Maxwell Poisson Models with Dynamic Dispersion. Journal of Agricultural, Biological,and Environmental Statistics. 18: 335–356.

[32.] Yang, W.H., Wikle, C.K., Holan, S.H., and Wildhaber, M.L. (2013) Ecological PredictionWith Nonlinear Multivariate Time-Frequency Functional Data Models. Journal of Agricul-tural, Biological, and Environmental Statistics. 18: 450–474.

[33.] Holan, S.H., Yang, W.H., Matteson, D.S., and Wikle, C.K. (2012) An Approach for Iden-tifying and Predicting Economic Recessions in Real-Time Using Time-Frequency FunctionalModels. (With Discussion) Applied Stochastic Models in Business and Industry. 28: 485–499.

[34.] Holan, S.H., Yang, W.H., Matteson, D.S., and Wikle, C.K. (2012) Rejoinder – An Approachfor Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Func-tional Models. Applied Stochastic Models in Business and Industry. 28: 504–505.

[35.] McElroy, T.S. and Holan, S.H. (2012) A Conversation with David Findley. Statistical Science.27: 594–606.

[36.] McElroy, T.S. and Holan, S.H. (2012) On the Computation of Autocovariances for General-ized Gegenbauer Processes. Statistica Sinica. 22: 1661–1687.

[37.] Wang, J. and Holan, S.H. (2012) Bayesian Multi-Regime Smooth Transition Regression withOrdered Categorical Variables. Computational Statistics and Data Analysis. 56: 4165–4179.

[38.] Cressie, N., Assuncao, R., Holan, S.H., Levine, M., Nicolis, O., Zhang, J., and Zou, J. (2012)Dynamical Random-Set Modeling of Concentrated Precipitation in North America. Statisticsand Its Interface. 5: 169–181.

[39.] Wang J., Holan, S.H., Nandram, B., Barbosa, W., Toto, C., and Anderson, E. (2012) ABayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys.Journal of Agricultural, Biological, and Environmental Statistics. 17: 84–106.

[40.] Arab, A., Holan, S.H., Wikle, C.K., and Wildhaber, M.L. (2012) Semiparametric BivariateZero-Inflated Poisson Models with Application to Studies of Abundance for Multiple Species.Environmetrics. 23: 183–196.

[41.] Veum, K.S., Goyne, K., Holan, S.H., and Motavalli, P. (2011) Assessment of Soil OrganicCarbon and Total Nitrogen Under Conservation Management Practices in the Central ClaypanRegion, Missouri, USA. Geoderma. 167/168: 188-196.

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[42.] Ferreira, M.A.R., Holan, S.H., and Bertolde, A. (2011) Dynamic Multiscale Spatio-TemporalModels for Gaussian Areal Data. Journal of the Royal Statistical Society – Series B. 73: 663–688.

[43.] Wikle, C.K. and Holan, S.H. (2011) Polynomial Nonlinear Spatio-Temporal Integro–Difference Equation Models. Journal of Time Series Analysis. 32: 339–350.

[44.] Wildhaber, M.L., Holan, S.H., Davis, G., Gladish, D., DeLonay, A., Papoulias, D., andSommerhauser, D. (2011) Evaluating Spawning Migration Patterns and Predicting SpawningSuccess of Shovelnose Sturgeon in the Lower Missouri River. Journal of Applied Ichthyology.27: 301–308.

[45.] Wildhaber, M.L., Holan, S.H., Bryan, J.L., Gladish, D., and Ellerseick, M. (2011) AssessingPower of Large River Fish Monitoring Programs to Detect Population Changes: The MissouriRiver Sturgeon Example. Journal of Applied Ichthyology. 27: 282–290.

[46.] Holan, S.H., Lund, R., and Davis, G. (2010) The ARMA Alphabet Soup: A Tour of ARMAModel Variants. Statistics Surveys. 4: 232–274.

[47.] Holan, S.H., Toth, D., Ferreira, M.A.R., and Karr, A. (2010) Bayesian Multiscale MultipleImputation With Implications for Data Confidentiality. Journal of the American StatisticalAssociation. 105: 564–577.

[48.] Holan, S.H., Wikle, C.K., Sullivan-Beckers, L., and Cocroft, R. (2010) Modeling ComplexPhenotypes: Generalized Linear Models Using Spectrogram Predictors of Animal Communi-cation Signals. Biometrics. 66: 914–924.

[49.] Holan, S.H. and McElroy, T.S. (2010) Tail Exponent Estimation via Broadband Log Density-Quantile Regression, Journal of Statistical Planning and Inference. 140: 3693–3708.

[50.] Roberts, C., Holan, S.H., and Monsell, B. (2010) Comparison of X-12 ARIMA Trading DayRegressors With Country Specific Regressors, Journal of Official Statistics. 26: 371–394.

[51.] Swearingen, J., Holan, S.H., Feldman, M., and Viator, J. (2010) Photoacoustic Discrimi-nation of Vascular and Pigmented Lesions Using Classical and Bayesian Methods, Journal ofBiomedical Optics. 15: 016019–1–9.

[52.] Holan, S.H., Davis, G., Wildhaber, M.L., DeLonay, A., and Papoulias, D. (2009) Hierarchi-cal Bayesian Markov Switching Models with Application to Predicting Spawning Success ofShovelnose Sturgeon. Journal of the Royal Statistical Society – Series C. 58: 47–64.

[53.] Holan, S.H., McElroy, T.S., and Chakraborty, S. (2009) A Bayesian Approach to Estimatingthe Long Memory Parameter. Bayesian Analysis. 4: 159–190.

[54.] McElroy, T.S. and Holan, S.H. (2009) Spectral Domain Diagnostics for Testing Model Prox-imity and Disparity in Time Series Data. Statistical Methodology. 6: 1–20.

[55.] McElroy, T.S. and Holan, S.H. (2009) A Local Spectral Approach for Assessing Time SeriesModel Misspecification. Journal of Multivariate Analysis. 100: 604–621.

[56.] McElroy, T.S. and Holan, S.H. (2009) A Nonparametric Test for Residual Seasonality. SurveyMethodology. 35: 67–83.

[57.] Holan, S.H., Wang, S., Arab, A., Sadler, E.J., and Stone, K. (2008) Semiparametric Geo-graphically Weighted Response Curves with Application to Site-Specific Agriculture. Journalof Agricultural, Biological, and Environmental Statistics. 13(4): 424–439.

[58.] Holan, S.H. and Viator J. (2008) Automated Wavelet Denoising of Photoacoustic Signals forCirculating Tumor Cell Detection and Burn Imaging. Physics in Medicine and Biology. 53:N227–N236.

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[59.] Holan, S.H. and Spinka, C.M. (2007) Maximum Likelihood Estimation for Joint Mean-Covariance Models From Unbalanced Repeated-Measures Data. Statistics and ProbabilityLetters. 77(3): 319–328.

[60.] Talbert, R.J., Holan, S.H., and Viator, J. (2007) Photoacoustic Discrimination of Viable andThermally Coagulated Blood Using a Two Wavelength Method for Burn Injury Monitoring.Physics in Medicine and Biology. 52: 1815–1829.

[61.] DeLonay, A., Papoulias, D., Wildhaber, M.L., Annis, M., Bryan, J., Griffith, S., Holan, S.H.,and Tillit, E. (2007) Use of Behavioral and Physiological Indicators to Evaluate SchaphirynchusSturegeou Spawning Success. Journal of Applied Ichthyology. 23: 428–435.

[62.] Phillips, C.D., Holan, S.H., Sherman, M., Hawes, C., and Spector, W. (2005) MedicareExpenditures for Residents in Assisted Living: Data from a National Study. Health ServicesResearch. 40(2): 373–388.

[63.] Hawes, C., Phillips, C.D., Holan, S.H., Sherman, M., and Hutchison, L. (2005) AssistedLiving in Rural America: Results from a National Survey. Journal of Rural Health. 21(2):131–139.

[64.] Phillips, C.D., Holan, S.H., Sherman, M., Leyk-Williams, M., and Hawes, C. (2004) Ruralityand Nursing Home Quality: Results from a National Sample of Admissions. American Journalof Public Health. 94(10): 1717–1722.

[65.] Spinka, C., Holan, S.H. and Spinka, H. (2004) Estimating Relative Luminosity for RHICSpin Physics. Nuclear Instruments and Methods in Physics Research A. A530: 537–541.

[66.] Hawes, C., Phillips, C.D., Rose, M., Holan, S.H., and Sherman, M. (2003) A National Surveyof Assisted Living Facilities. The Gerontologist. 43: 875–882.

REFEREED BOOK CHAPTERS:

[1.] Holan, S.H. and Wikle, C.K. (2016) Hierarchical Dynamic Generalized Linear Mixed Modelsfor Discrete–Valued Spatio-Temporal Data. Handbook of Discrete–Valued Time Series, RichardA. Davis, Scott H. Holan, Robert Lund, and Nalini Ravishanker (Eds.), 327–348, Chapman &Hall/CRC.

[2.] Lund, R., Holan, S.H., and Livsey, J. (2016) Long Memory Discrete–Valued Time Series.Handbook of Discrete–Valued Time Series, Richard A. Davis, Scott H. Holan, Robert Lund,and Nalini Ravishanker (Eds.), 447–458, Chapman & Hall/CRC.

[3.] Holan, S.H. and Wikle, C.K. (2012) Semiparametric Dynamic Design of Monitoring Networksfor Non-Gaussian Spatio-Temporal Data. Spatio-temporal Design: Advances in Efficient DataAcquisition, Jorge Mateu and Werner Muller (Eds.), 269–284, Wiley.

[4.] Holan, S.H. and McElroy, T.S. (2012) Bayesian Seasonal Adjustment of Long Memory TimeSeries. Economic Time Series: Modeling and Seasonality, William R. Bell, Scott H. Holan,and Tucker S. McElroy (Eds.), 403–428, Chapman & Hall/CRC.

[5.] McElroy, T.S. and Holan, S.H. (2012) The Error in Business Cycle Estimates Obtained fromSeasonally Adjusted Data. Economic Time Series: Modeling and Seasonality, William R. Bell,Scott H. Holan, and Tucker S. McElroy (Eds.), 109–131, Chapman & Hall/CRC.

[6.] Ferreira, M.A.R., Bertolde, A., and Holan, S.H. (2010) Analysis of Economic Data WithMultiscale Spatio-Temporal Models. Handbook of Applied Bayesian Analysis, Anthony O’Hagan and Mike West (Eds.), 295–318, Oxford University Press.

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SUBMITTED PUBLICATIONS:

[1.] Simpson, M., Holan, S.H., Wikle, C.K., and Bradley, J.R. (2017) Interpolating Distributionsfor Populations in Nested Geographies using Public-use Data with Application to the AmericanCommunity Survey. (Under Invited Revision – Journal of the American Statistical Association– A&CS).

[2.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2017) Spatio-Temporal Models for Big Multino-mial Data using the Conditional Multivariate Logit-Beta Distribution. (Under Invited Revision– Journal of Time Series Analysis).

[3.] Chen, J., Micheas, A. C., and Holan, S.H. (2017) Bayesian Modeling and Decision Theoryfor Non-Homogeneous Poisson Point Processes. (Under Invited Revision – Bayesian Analysis)

[4.] Wu, G, Holan, S.H., Avril, A., and Waldstrom, J. (2017) A Bayesian SemiparametricJolly-Seber Model with Individual Heterogeneity: An Application to Migratory Mallards atStopover. (Under Invited Revision – Journal of the American Statistical Association – A&CS).

[5.] Bradley, J.R., Holan, S.H., and Wikle, C.K. (2016) Bayesian Hierarchical Models with Conju-gate Full-Conditional Distributions for Dependent Data from the Natural Exponential Family.(Under Invited Revision – Journal of the American Statistical Association – T&M).

INVITED DISCUSSIONS IN REFEREED JOURNALS:

[1.] Wikle, C.K., and Holan, S.H. (2015) Comment on “Semiparametric Bayesian Density Esti-mation with Disparate Data Sources: A Meta-Analysis of Global Childhood Undernutrition”by Finncane, M. M., Paciorek, C. J., Stevens, G. A., and Ezzati, M., Journal of the AmericanStatistical Association. 110: 901–903.

[2.] Wikle, C.K., Holan, S.H., and Hooten, M. (2013) Guest Editors’ Introduction to the SpecialIssue on “Modern Dimension Reduction Methods for Big Data Problems in Ecology.” Journalof Agricultural, Biological, and Environmental Statistics. 18: 271–273.

[3.] Cressie, N. and Holan, S.H. (2011) Editorial: Special Issue on Time Series in the Environ-mental Sciences. Journal of Time Series Analysis. 32: 337–338.

[4.] Holan, S.H and Wikle, C.K. (2010) Comments on: “Bayesian Source Detection and Param-eter Estimation of a Plume Model Based on Sensor Network Measurements.” by C. Huang etal. Applied Stochastic Models in Business and Industry. 26: 353–357.

[5.] Wikle, C.K. and Holan, S.H. (2009) Discussion of “Approximate Bayesian Inference forLatent Gaussian Models by Using Integrated Nested Laplace Approximations.” by Rue, H.,Martino, S. and Chopin, N. Journal of the Royal Statistical Society – Series B. 71(2): 384.

INVITED BOOK REVIEWS:

[1.] Holan, S.H. (2009) Review of Time Series Analysis: With Applications in R – 2nd edition,by Jonathan D. Cryer and Kung-Sik Chan, Journal of the American Statistical Association.104: 1727.

[2.] Holan, S.H. (2009) Review of Nonlinear Time Series: Semiparametric and NonparametricMethods, by Jiti Gao, Journal of the American Statistical Association. 104: 862–863.

[3.] Holan, S.H. (2008) Review of Long-Memory Time Series, by Wilfredo Palma, Journal of theAmerican Statistical Association. 103: 1715–1716.

Scott H. Holan 10

OTHER PUBLICATIONS:

[1.] Raim, A.M., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2017) A Model Selection Studyfor Spatio-Temporal Change of Support, ASA Proceedings of the Joint Statistical Meetings,American Statistical Association (Alexandria, VA).

[2.] Davis, R.A., Holan, S.H., Lund, R.B., and Ravishanker, N. (2016) Preface – Handbook ofDiscrete-Valued Time Series, Richard A. Davis, Scott H. Holan, Robert B. Lund, and NaliniRavishanker (Eds.), xi–xiii, Chapman & Hall/CRC.

[3.] National Research Council (2015), Realizing the Potential of the American Community Sur-vey: Challenges, Tradeoffs, and Opportunities. Panel on Addressing Priority Technical Issuesfor the Next Decade of the American Community Survey, The National Academies Press.(Contributing Author).

[4.] Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015) Multiscale Analysis of Survey Data: Re-cent Developments and Exciting Prospects. Statistics Views, Wiley. www.statisticsviews.com

/details/feature/7657481/Multiscale-Analysis-of-Survey-Data-Recent-Developments-and-

Exciting-Prospects.html

[5.] McElroy, T.S. and Holan, S.H. (2014) Fast Estimation of Time Series with Multiple Long-Range Persistencies, ASA Proceedings of the Joint Statistical Meetings, American StatisticalAssociation (Alexandria, VA).

[6.] Cressie, N., Holan, S.H., and Wikle, C.K. (2013) How can survey estimates of small areasbe improved by leveraging social-media data? The Survey Statistician, 68:

http://isi.cbs.nl/iass/N68.pdf

[7.] Wikle, C.K. Holan, S.H., and Cressie, N. (2013) Hierarchical Spatio-Temporal Models andSurvey Research. Statistics Views, Wiley. http://www.statisticsviews.com/details/feature/

4730991/Hierarchical-Spatio-Temporal-Models-and-Survey-Research.html

[8.] Bell, W.R., Holan, S.H., and McElroy, T.S. (2012) Preface – Economic Time Series: Mod-eling and Seasonality, William R. Bell, Scott H. Holan and Tucker S. McElroy (Eds.), ix–xii,Chapman & Hall/CRC.

[9.] Bryan, J.L., Wildhaber, M.L., Gladish, D., Holan, S.H., and Ellerseick, M. (2010) The PowerTo Detect Trends in Missouri River Fish Populations Within the Pallid Sturgeon PopulationAssessment Program: U.S. Geological Survey Open-File Report 20101020, 414 p.

[10.] McElroy, T.S. and Holan, S.H. (2009) The Detection of Cycles in Raw and Seasonally Ad-justed Data, ASA Proceedings of the Joint Statistical Meetings, American Statistical Associa-tion (Alexandria, VA).

[11.] McElroy, T.S. and Holan, S.H. (2009) Using Spectral Peaks to Detect Seasonality, Proceedingsof the Federal Committee on Statistical Methodology (FCSM - Research Conference Papers).

[12.] Holan, S.H., McElroy, T.S., and Chakraborty, S. (2008) Bayesian FEXP Models for LongMemory Time Series Analysis, ASA Proceedings of the Joint Statistical Meetings, AmericanStatistical Association (Alexandria, VA).

[13.] Holan, S.H. and Viator, J. (2007) Automated Wavelet Denoising of Photoacoustic Signalsfor Burn Depth Image Reconstruction, Proceedings of SPIE, Vol. 6347 - San Jose, CA.

[14.] McElroy, T.S. and Holan, S.H. (2007) Model Fitting Diagnostic Tests Based on the Log De-terminant of the Sample Covariance Matrix, ASA Proceedings of the Joint Statistical Meetings,American Statistical Association (Alexandria, VA).

[15.] McElroy, T.S. and Holan, S.H. (2006) Assessing Time Series Model Misspecification ThroughLocal Frequency Domain Methods, ASA Proceedings of the Joint Statistical Meetings, Ameri-can Statistical Association (Alexandria, VA).

Scott H. Holan 11

[16.] Evans, T., Holan, S.H., and McElroy, T.S. (2006) Evaluating Measures for Assessing SpectralPeaks, ASA Proceedings of the Joint Statistical Meetings, American Statistical Association(Alexandria, VA).

EDITORIAL ACTIVITIES:

Editor, International Statistical Review (ISR), 2015 – (Co-Editor-in-Chief 8/2018–)

Associate Editor, Journal of Nonparametric Statistics, 2011 –

Associate Editor, Journal of Time Series Analysis, 2011 –

Associate Editor, Journal of Agricultural, Biological, and Environmental Statistics, 2011 –2016

Journal of Time Series Analysis, Special Issue on “Recent Advances in Spatio-TemporalStatistics.” Co-Guest Editor (with Christopher K. Wikle), 2017–2018.

Journal of Agricultural, Biological, and Environmental Statistics, Special Issue on “ModernDimension Reduction Methods for Big Data Problems in Ecology.” Co-Guest Editor (withMevin B. Hooten and Christopher K. Wikle), 2012–2013.

Journal of Time Series Analysis, Special Issue on “Time Series in the Environmental Sci-ences.” Co-Guest Editor (with Noel Cressie), 2010–2011.

INVITED PRESENTATIONS:

“Interpolating Distributions for Populations in Nested Geographies using Public-use Datawith Application to the American Community Survey,” The 62nd World Statistics Congressof the International Statistical Institute (ISI), Kuala Lumpur, Malaysia, August 2019.

Invited Discussant, International Statistical Review (ISR) Invited Session, The 62nd WorldStatistics Congress of the International Statistical Institute (ISI), Kuala Lumpur, Malaysia,August 2019.

“Interpolating Distributions for Populations in Nested Geographies using Public-use Datawith Application to the American Community Survey,” Joint Statistical Meetings, Denver,CO, July 2019.

TBD, International Chinese Statistical Association (ICSA) China Conference, Nankai Uni-versity, Tianjin, China, July 2019.

“Interpolating Distributions for Populations in Nested Geographies using Public-use Datawith Application to the American Community Survey”, (Keynote Speaker) 6th AfricanInternational Conference on Statistics, Arsi University, Ethiopia, Africa, May 2019.

TBD, NORC, Chicago, IL, February 2019.

“Interpolating Distributions for Populations in Nested Geographies using Public-use Datawith Application to the American Community Survey,” Florida State University, Tallahas-see, FL, November 2018.

“Geospatial Considerations on Data Quality,” FCSM Geospatial Interest Group Workshop,Bureau of Labor Statistics, Washington, D.C., October 2018.

“Utilizing Dependent Data Models for Official Statistics Applications,” U.S. Census Bureau,Washington, D.C., October 2018.

“Improving ACS Estimation with Multiple Surveys and Third Party Data,” Workshop onImproving the American Community Survey, The National Academies of Sciences, Wash-ington, D.C., September 2018.

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“Panel Discussion: Interdisciplinary Research” (Invited Panelist), Chair’s Retreat, Universityof Missouri, Columbia, MO, August 2018.

“Computationally Efficient Multivariate Spatio-Temporal Models for High-DimensionalCount-Valued Data,” ISBIS 2018 Meeting, Athens, Greece, July 2018.

“Computationally Efficient Bayesian Hierarchical Models for High-Dimensional DependentMultinomial Data,” ISBA World Meeting, Edinburgh, UK, June 2018.

“Computationally Efficient Distribution Theory for Bayesian Inference of High-DimensionalDependent Count-Valued Data,” (Keynote Speaker) 5th African International Conferenceon Statistics, University of Botswana, Africa, March 2018.

“Cross Continent Research and Collaboration in Statistics: Challenges and Opportunities,”(Invited Panelist) 5th African International Conference on Statistics, University ofBotswana, Botswana, Africa, March 2018.

“Interpolating Distributions for Populations in Nested Geographies using Public-use Datawith Application to the American Community Series,” U.S. Census Bureau, WashingtonD.C., February 2018.

“Recent Advances in Spatial and Spatio-Temporal Change of Support for Official Statistics,”Second FCSM/WSS Workshop on Quality of Integrated Data: Reporting on Quality Issuesin Data Processing, Washington D.C., January 2018.

“Computationally Efficient Multivariate Spatio-Temporal Models for High-DimensionalCount-Valued Data,” University of Georgia, Athens, GA, November 2017.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Applicationto Longitudinal Employer-Household Dynamics,” University of Alabama, Tuscaloosa, AL,November 2017.

“Computationally Efficient Distribution Theory for Bayesian Inference of High-DimensionalDependent Count-Valued Data,” NBER–NSF Time Series Conference, Northwestern Uni-versity, Evanston, IL, September 2017.

ISI–WSC 2017 Tutorial Session, “How to Prepare Successful Grant Applications” (InvitedPanelist), The 61st World Statistics Congress of the International Statistical Institute (ISI),Marrakech, Morocco, July 2017.

“Small Area Estimation for High-Dimensional Multivariate Spatio-Temporal Count Data,”The 61st World Statistics Congress of the International Statistical Institute (ISI), Mar-rakech, Morocco, July 2017.

“Small Area Estimation for High-Dimensional Multivariate Spatio-Temporal Count Data,”Small Area Estimation Conference, Paris, France, July 2017.

“Multivariate Spatio-Temporal Survey Fusion with Application to the American CommunitySurvey and Local Area Unemployment Statistics,” ISBIS 2017 Meeting on Statistics inBusiness and Industry, IBM T. J. Watson Research Center in Yorktown Heights, NY, June2017.

“Bayesian Analysis of Spatially-Dependent Functional Responses with Spatially-DependentMulti-Dimensional Functional Predictors,” Southern Regional Conference on Statistics(SRCOS), Jekyll Island, GA, June 2017.

“Spatio-Temporal Change of Support with Application to American Community SurveyMulti-Year Period Estimates,” Worcester Polytechnic Institute (WPI), Worcester, MA,April 2017.

Scott H. Holan 13

“Spatio-Temporal Change of Support With Application to American Community SurveyMulti-Year Period Estimates,” Eastern North American Region Biometric Society (ENAR),Washington DC, March 2017.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Applicationto Longitudinal Employer-Household Dynamics,” University of Washington, Center forStatistics and the Social Sciences (CSSS), Seattle WA, February 2017.

“The Cepstral Model for Multivariate Time Series: The Vector Exponential Model,” NBER-NSF Time Series Conference, Columbia University, NY, September 2016 (Poster).

Panel Session on New Developments in Small Area Estimation, Small Area Estimation Con-ference (Invited Panelist), Maastricht, The Netherlands, August 2016.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application toLongitudinal Employer-Household Dynamics,” Small Area Estimation Conference, Maas-tricht, The Netherlands, August 2016.

“Spatio-Temporal Change of Support With Application to American Community SurveyMulti-Year Period Estimates,” Joint Statistical Meetings, Chicago IL, August 2016.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application toLongitudinal Employer-Household Dynamics,” International Chinese Statistical Associa-tion (ICSA) Applied Symposium, Atlanta, GA, June 2016.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application toLongitudinal Employer-Household Dynamics,” ISBIS 2016 Meeting on Statistics in Businessand Industry, Barcelona, Spain, June 2016.

“Spatio-Temporal Change of Support With Application to American Community SurveyMulti-Year Period Estimates,” NCRN Fall Meeting, Bureau of Labor Statistics, WashingtonD.C., December 2015.

“Recent Advances and Future Directions in Spatial and Spatio-Temporal Statistics for Fed-eral Data: Academic Partnerships and the NCRN,” Geospatial Statistics, Tools, Data,Practices, Opportunities and Challenges in the Federal Agencies (Workshop), WashingtonDC, October 2015.

“Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Applicationto Longitudinal Employer-Household Dynamics,” NBER-NSF Time Series Conference, Vi-enna, Austria, September 2015.

“Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis,”Joint Statistical Meetings, Seattle WA, August 2015.

“Bayesian Analysis of Spatially-Dependent Functional Responses with Spatially-DependentMulti-Dimensional Functional Predictors,” International Chinese Statistical Association(ICSA) Applied Symposium/Graybill Conference, Fort Collins CO, June 2015.

“Time-Frequency Estimation and Time-Frequency Functional Data Models,” Indo-US Work-shop on Time Series Analysis, Pune India, May 2015.

“Models for Multiscale Spatially-Referenced Count Data,” NCRN Spring Meeting, NationalAcademy of Science, Washington D.C., May 2015.

“Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis,”University of Illinois, Urbana-Champaign IL, April 2015.

“Soil Property Estimation and Design for Agroecosystem Management using HierarchicalGeospatial Functional Data Models,” Environmental Protection Agency, Washington D.C.,March 2015.

Scott H. Holan 14

“Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dis-persion,” Climate Corporation, San Francisco, January 2015.

“Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis,”Colorado School of Mines, Golden CO, November 2014.

“Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis,”University of Maryland, Baltimore County MD, October 2014.

“An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Joint Statistical Meetings, Boston MA, August 2014.

JABES Showcase – Impact of Advanced Statistical Methods on Experiments and Studies inAgricultural and Environmental Sciences (Invited Discussant), Joint Statistical Meetings,Boston, August 2014.

“Bayesian Dynamic Time-Frequency Estimation,” Twelfth World Meeting of ISBA, Cancun,Mexico, July 2014.

“An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Seminar on Bayesian Inference in Econometrics and Statis-tics (SBIES), University of Chicago Graduate School of Business – Chicago, May 2014.

“Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Mod-els,” University of Missouri, Department of Statistics, April 2014.

“A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Sur-veys,” U.S. Census Bureau – Center for Statistical Research and Methodology (CSRM),March 2014.

“Spatial Fay-Herriot Models for Small Area Estimation With Functional Covariates,” Com-putational Methods for Censuses and Surveys (SAMSI), Washington D.C., January 2014.

“Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dis-persion,” 6th International Conference of the ERCIM, London, England, December 2013.

“Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Mod-els,” North Carolina State University, November 2013.

“Recent Advances in Spatial Methods for Federal Surveys,” NISS Workshop: GeospatialMethods for Federal Surveys, Washington D.C., September 2013.

“Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dis-persion,” The 59th World Statistics Congress of the International Statistical Institute (ISI),Hong Kong, China, August 2013.

“Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dis-persion,” Joint Statistical Meetings, Montreal, Canada, August 2013.

“Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Mod-els,” University of Connecticut, April 2013.

“A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Sur-veys,” Institute of Public Policy and the Truman School of Public Affairs, University ofMissouri, March 2013.

“An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” NBER-NSF Time Series Conference, Texas A&M Univer-sity, October 2012 (Poster).

“Hierarchical Time-Frequency Functional Data Models,” Cornell Univ., September 2012.

Scott H. Holan 15

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” Clem-son University, April 2012.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” RiceUniversity, March 2012.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” DukeUniversity, February 2012.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” TexasA&M University, January 2012.

“Improving the Interpretability and Usability of the American Community Survey ThroughHierarchical Multiscale Spatio-Temporal Statistical Models,” U.S. Census Bureau,

December 2011.

“On the Computation of Autocovariances for Generalized Gegenbauer Processes,” NBER-NSF Time Series Conference, Michigan State University, September 2011 (Poster).

“A Flexible Hierarchical Approach to Modeling Discrete-Valued Spatio-Temporal Data,”Joint Statistical Meetings, Miami FL, August 2011.

“A Bayesian Approach to Seasonal Adjustment of Long Memory Time Series,” SouthernRegional Conference on Statistics (SRCOS), McCormick SC, June 2011.

“A Bayesian Approach to Seasonal Adjustment of Long Memory Time Series,” IISA Confer-ence on Probability, Statistics, and Data Analysis, Raleigh NC, April 2011.

“Hierarchical Spatial and Spatio-Temporal Statistical Modeling for Environmental Appli-cations,” ASA/CSSA/SSSA (American Society of Agronomy/Crop Science Society/SoilScience Society of America) International Annual Meeting, Long Beach CA, November2010.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” Uni-versity of Chicago - Booth School of Business, November 2010.

“A Bayesian Approach to Seasonal Long Memory,” U.S. Census Bureau – Statistical ResearchDivision (SRD), July 2010.

“Bayesian Mark-Recapture Models for Fish Survival and Population Estimation for Use inMulti-Scale Modeling of Riverine Ecosystems,” 3rd USGS Modeling Conference, BroomfieldCO, June 2010.

“Semiparametric Geographically Weighted Response Curves with Application to Site-SpecificAgriculture,” SAMSI, October 2009.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” NorthCarolina State University, September 2009.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” U.S.Census Bureau – Statistical Research Division (SRD), May 2009.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” Semi-nar on Bayesian Inference in Econometrics and Statistics (SBIES), Washington University– St. Louis, May 2009.

“Hierarchical Bayesian Markov Switching Models with Application to Predicting SpawningSuccess of Shovelnose Sturgeon,” Washington University - St. Louis, December 2008.

“Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality,” Wash-ington Statistical Society, November 2008.

Scott H. Holan 16

“A Bayesian Approach to Estimating the Long Memory Parameter,” U.S. Census Bureau –Statistical Research Division (SRD), November 2008.

“Hierarchical Bayesian Markov Switching Models with Application to Predicting SpawningSuccess of Shovelnose Sturgeon,” Georgetown University, November 2008.

“Hierarchical Bayesian Markov Switching Models with Application to Predicting SpawningSuccess of Shovelnose Sturgeon,” American University, November 2008.

“Hierarchical Bayesian Markov Switching Models with Application to Predicting SpawningSuccess of Shovelnose Sturgeon,” Environmental Sensor Networks Progress and ChallengesWorkshop - SAMSI, October 2008.

“A Bayesian Approach to Estimating the Long Memory Parameter,” Joint Statistical Meet-ings, August 2008.

“Semiparametric Geographically Weighted Response Curves with Application to Site-SpecificAgriculture,” 11th New Researchers Conference in Statistics and Probability, August 2008.

“Bayesian Inference for the Long Memory Parameter in FEXP Models,” Seventeenth AnnualMeeting of the Midwest Econometrics Group, October 2007.

“Assessing Time Series Model Misspecification Through Local Frequency Domain Methods,”Bureau of Labor Statistics, July 2007.

“A Spectral Approach for Locally Assessing Time Series Model Misspecification with Appli-cation to Nonstationary Data,” Northern Illinois University, April 2007.

“Hierarchical Bayesian Markov Switching Models with Application to Predicting SpawningBehavior of Shovelnose Sturgeon,” The University of Texas M.D. Anderson Cancer Center,March 2007.

“A Spectral Approach for Locally Assessing Time Series Model Misspecification with Appli-cation to Nonstationary Data,” George Washington University, February 2007.

“Semiparametric Geographically Weighted Response Curves,” University of Virginia - De-partment of Systems and Information Engineering, November 2006.

“A Spectral Approach for Locally Assessing Time Series Model Misspecification,” ClemsonUniversity, November 2006.

“A Spectral Approach for Locally Assessing Model Misspecification,” Joint Statistical Meet-ings, August 2006.

“Nonparamentric and Model Based Tests for Assessing Spectral Peaks,” London School ofEconomics, April 2006.

“Statistical Methods for Photoacoustic Sensors and Imaging of Burn Injury,” Univeristy ofMissouri - Life Sciences Center, April 2006.

“A Nonparametric Test for Assessing Spectral Peaks,” Bureau of Labor Statistics, February2006.

“A Nonparametric Test for Assessing Spectral Peaks,” SCMA (Statistics, CombinatoricsMathematics and Applications), December 2005.

“Diagnostics for Seasonal Adjustment Decomposition Components,” Bureau of Labor Statis-tics, July 2005.

“Time Series Exponential Models: Theory and Methods,” Novartis Pharmaceuticals, March2004.

—, Department of Mathematics and Statistics, University of Massachusetts, March 2004.

—, Department of Statistics, University of Missouri, February 2004.

Scott H. Holan 17

—, Department of Mathematics and Statistics, Utah State, February 2004.

—, Department of Statistics, NC State, February 2004.

—, Department of Mathematics and Statistics, Arizona State University, February 2004.

—, Bureau of Labor Statistics, January 2004.

—, IBM - T.J.Watson Research Center, January 2004.

CONTRIBUTED PRESENTATIONS:

“Computationally Efficient Bayesian Hierarchical Models for High-Dimensional DependentMultinomial Data,” Joint Statistical Meetings, (Topic Contributed Session), Vancouver,BC, CA, August 2018.

“Multivariate Spatio-Temporal Survey Fusion with Application to the American Commu-nity Survey and Local Area Unemployment Statistics,” Joint Statistical Meetings, (TopicContributed Session), Baltimore, MD, August 2017.

“Flexible Spectral Models for Multivariate Time Series,” Joint Statistical Meetings,

(Topic Contributed Session), San Diego, CA, August 2012.

“On the Seasonal Adjustment of Long Memory Time Series,” Joint Statistical Meetings,(Topic Contributed Session), ,Vancouver BC, CA, August 2010.

“A Bayesian Approach to Seasonal Long Memory,” Joint Statistical Meetings, (Topic Con-tributed Session), Washington, D.C., August 2009.

“A Local Goodness-of-Fit Diagnostic Based on the Log Determinant of the Sample CovarianceMatrix,” Joint Statistical Meetings, (Topic Contributed Session), Salt Lake City, UT, July2007.

“A Spectral Approach for Locally Assessing Model Misspecification,” International Sympo-sium on Forecasting, Santander, Spain, June 2006.

“Variance Covariance Estimation With an Application to Intercellular Signaling,” Joint Sta-tistical Meetings, August 2005.

“Variance Covariance Estimation With an Application to Intercellular Signaling,” EasternNorth American Region/International Biometric Society (ENAR), March 2005.

“Order Selection for Short Memory Exponential Time Series Models,” Joint Statistical Meet-ings, August 2003.

“Transitions In Community-Based Care: Movement From Assisted Living To A Nursing HomeIn A National Sample,” NIEHS Site Visit, Texas A&M University, May 2001.

PROFESSIONAL MEMBERSHIPS:

American Association for the Advancement of Science (AAAS)

American Statistical Association (ASA; Lifetime Member)

ASA Sections: Bayesian Statistical Science, Business and Economic Statistics, GovernmentStatistics, Nonparametric Statistics, Statistics and the Environment, Survey ResearchMethods.

Institute of Mathematical Statistics (IMS; Lifetime Member)

International Society for Bayesian Analysis (ISBA; Lifetime Member)

International Statistical Institute (ISI)

Scott H. Holan 18

ISI Sections: International Association of Survey Statisticians (IASS), International Associa-tion for Official Statistics (IOAS), International Society for Business and Industrial Statis-tics (ISBIS), The International Environmetrics Society (TIES).

PROFESSIONAL SERVICE:

2019–2021 American Statistical Association (ASA) Karl E. Peace Award for OutstandingStatistical Contribution (Committee Member).

2019 Invited Session Co-Organizer, “Recent Advances in Statistical Modeling forData with Complex Spatial and Spatio-Temporal Dependence Structures” –International Chinese Statistical Association (ICSA) China Conference,Nankai University, Tianjin, China

2019 Invited Session Organizer – International Statistical Institute (ISI) World Statis-tics Congress (WSC), “Modeling of Multivariate and Non-Gaussian DependentData with Applications,” Kuala Lumpur, Malaysia.

2018–2019 International Statistical Institute (ISI) World Statistics Congress (WSC) 2019Short Course Committee Member.

2018 Invited Session Organizer/Chair – Joint Statistical Meetings, “Recent Advancesin Modeling Complex Dependent Data.”

2017 Savage Award Committee (International Society for Bayesian Analysis) – Com-mittee Member.

2017–2019 H.O. Hartley Award Committee (Texas A&M University) – Committee Mem-ber.

2017 Memorial Session Organizer – Joint Statistical Meetings, “Memorial Session forEmanuel Parzen.”

2017 Topic Contributed Session Organizer – Joint Statistical Meetings, “Recent Ad-vances in High-Frequency and High-Dimensional Time Series.”

2017 Topic Contributed Session Organizer – Joint Statistical Meetings, “Advancesin Spatial and Spatio-Temporal Methodology with Applications to OfficialStatistics.”

2017 Invited Session Organizer – Small Area Estimation Conference 2017, “Account-ing for Dependence in Small Area Estimation,” Paris, France.

2016 Session Chair – Small Area Estimation Conference, “Temporal and Spatial Mod-els.”

2016 Spatial and Spatio-Temporal Design and Analysis for Official Statistics – Work-shop, MO, Co-Chair – Scientific Organizing Committee.

2016 Associate Program Chair (ASA) – Joint Statistical Meetings.

2016 Invited Session Organizer/Chair – Joint Statistical Meetings, “Recent Advancesin Discrete-Valued Time Series.”

2016 Invited Session Organizer – Joint Statistical Meetings, “Advances in StatisticalMethods for Dissemination and Analysis of Official Statistics.”

2016 Topic Contributed Session Organizer – Joint Statistical Meetings, “Recent Ad-vances and Applications of Spatial and Spatio-Temporal Models for OfficialStatistics.”

2015–2020 American Statistical Association (ASA) Committee on Funded Research (Co-Chair 2019–2020).

Scott H. Holan 19

2015 Session Organizer – NSF-Census Research Network (NCRN) Fall Meeting, DC,“Small Domain Estimation and Visualization of Uncertainty in Small AreaData.”

2015 Associate Program Chair Elect (ASA) - Joint Statistical Meetings.

2015 Invited Session Organizer – Joint Statistical Meetings, “Recent Advances inStatistical Methodology for Small Area Estimation and Federal Surveys.”

2015 Invited Session Organizer – NCRN Spring Meeting, “Geographic Aspects ofStatistics.”

2014 NBER/NSF Time Series Conference, Scientific Committee Member.

2014 Session Organizer/Chair – NSF-Census Research Network (NCRN) Fall Meet-ing, NY, “Geospatial Statistics.”

2014 Invited Session Organizer – Joint Statistical Meetings, “Bayesian Time Seriesand Dynamic Models.”

2014 Topic Contributed Session Organizer – Joint Statistical Meetings, “Recent Ad-vances in Spatial Methodology for Federal Surveys.”

2014 Session Chair – Joint Statistical Meetings, “Employment Dynamics: Data andStatistical Modeling.”

2012–2016 NSF-Census Research Network Coordination Office (NCRN-CO) Steering Com-mittee Member.

2012 Program Chair – Business and Economic Section (ASA) – Joint StatisticalMeetings.

2012 Topic Contributed Session Organizer – Joint Statistical Meetings, “Recent Ad-vances in Multivariate and Multiple Time Series.”

2012 Session Chair – Joint Statistical Meetings, “Spatial Socio-Demographics.”

2011 Program Chair Elect – Business and Economic Section (ASA) - Joint StatisticalMeetings.

2011 Session Chair – Joint Statistical Meetings, “Business Applications.”

2011–2014 Affiliate – Program in Spatial Statistics and Environmental Statistics (SSES;The Ohio State University).

2010 Invited Session Organizer/Chair – Joint Statistical Meetings, “Bayesian Meth-ods in Time Series Econometrics.”

2010 Statistical Assessment of Environmental Risk Workshop, SAMSI/SPRUCE,NC, Organizing Committee Member.

2009–2010 Program on Space-Time Analysis for Environmental Mapping, Epidemiologyand Climate Change, SAMSI, NC, Research Group Leader.

2009 Session Chair – Joint Statistical Meetings, “Topics in Seasonal Time Series.”

2008 Session Chair – Joint Statistical Meetings, “Model Optimisation and Evalua-tion.”

2006– Statistical Consultant – United States Geological Survey (USGS).

2005–2007 Chapter President, Mid-Missouri Chapter - American Statistical Association

2001–2004 Chapter Treasurer, Southeast Texas Chapter - American Statistical Association

Scott H. Holan 20

Refereed Articles for: Annals of Applied Statistics; Annals of Statistics; Applied StochasticModels in Business and Industry; Bayesian Analysis; Biometrics; Biometrika; Biostatistics;Canadian Journal of Statistics; Communications in Statistics: Theory and Methods; Com-putational Statistics and Data Analysis; Demography; Ecological Applications; Economet-rics and Statistics; Electronic Journal of Statistics; Environmental and Ecological Statistics;Environmetrics; International Statistical Review; Journal of Agricultural, Biological, andEnvironmental Statistics; Journal of the American Statistical Association; Journal of Ap-plied Ecology; Journal of Computational and Graphical Statistics; Journal of EnvironmentalQuality; Journal of Forecasting; Journal of Official Statistics; Journal of Multivariate Anal-ysis; Journal of the Royal Statistical Society – Series A; Journal of the Royal StatisticalSociety – Series B; Journal of Statistical Computation and Simulation; Journal of Sta-tistical Planning and Inference; Journal of Survey Statistics and Methodology; Journal ofStatistical Theory and Practice; Journal of Time Series Analysis; Mathematical Problemsin Engineering; North American Journal of Fisheries Management; Scandinavian Journalof Statistics; Spatial Statistics; Statistica Sinica; Statistics and Computing; Statistics andProbability Letters; Survey Methodology; TEST; The American Statistician; Transactionson Database Systems; WIREs Computational Statistics.

External Reviewer for:National Academies of Sciences – Confidential Report Reviewer (2017)Promotion and Tenure (P&T)/Promotion Cases (1 in 2015, 3 in 2016, 2 in 2017)National Security AgencySuperior Council of the National Fund for Scientific & Technological Develop-ment (FONDECYT) (Government of Chile)

National Science FoundationNational Endowment for the Humanities (NEH)Swiss National Science Foundation (SNSF) – Division of Mathematics, Physicaland Engineering Sciences

Bureau of Labor Statistics (BLS)John Wiley & Sons (Statistics Book Proposal Review)Chapman & Hall/CRC (Statistics Book Proposal Review)Ph.D. thesis committee of Nusrat Jahan at Mississippi State University (JaneHarvill - Director)

Internal Reviewer for:University of Missouri Research Board (Internal Grant)United States Census BureauUnited States Geological Survey

POSTDOCS DIRECTED:

Matthew Simpson (2015–2018) NSF-Census Research Network

Jonathan R. Bradley (2013–2016) NSF-Census Research Network

Harrison Quick (2013–2014) NSF-Census Research Network

Aaron T. Porter (2012–2014) NSF-Census Research Network

Jianqiang (Jay) Wang (2009–2010), Statistical Multi-Source Predictive Models and ErrorEstimates with Application to Major USDA Crop Production Forecasts and Estimates(Faculty Mentor - NISS-NASS Postdoc).

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Ph.D. STUDENTS DIRECTED:

Guohui Wu (May 2014), Flexible Bayesian Hierarchical Models for Discrete-Valued Spatio-Temporal Data.

Wen-Hsi Yang (August 2013), Hierarchical Nonlinear, Multivariate, and Spatially-

Dependent Time-Frequency Functional Models (Co-Directed with C.K. Wikle).

Yuelei Sui (Current), Nonstationary Bayesian Time Series Models with Time-Varying Vari-ance and Regime-Switching.

Christopher Hassett (Current; Co-Directed with A.C. Micheas)

Jiaxun Chen (Current; Co-Directed with A.C. Micheas)

Haya Aldossary (Current)

Paul Parker (Current)

MASTERS STUDENTS DIRECTED:

Dillon Gash (2019), TBD

Mary Jost (2016), Small Area Estimation: Fay-Herriot Models With Spatial Dependence forCounty Level American Community Survey Data.

Mitchell Tucker (2016),Using Nonlinear and Semiparametric Models to Provide Evidence Re-garding the Existence of Peer Effects, (M.S. Stat./Econ. – Co-Directed with M. Podgursky).

Hui Wang (May 2014), Estimation of the Term Structure of Interest Rates Using BayesianSemiparametric Splines, (M.S. Stat./Econ. – Co-Directed with X. Ni).

Daniel Gladish (May 2009), Assessing Power of Large River Fish Monitoring Programs toDetect Population Changes.

Corrie Ann Hutchinson (May 2009), Semiparametric Models of Species Abundance.

Daniel Sommerhauser (May 2009), Clustering Sparsely Sampled Functional Data – An Ap-plication to Shovelnose Sturgeon Migration Patterns.

Christopher Roberts (August 2008), Comparison of X-12 ARIMA Trading Day Regressors toCountry Specific Regressors.

UNDERGRADUATE RESEARCH ASSISTANTS:

Jie Lou (5/2017 – 7/2017)

Nathan Berliner (6/2012 – 8/2013; Co-Supervised with C.K. Wikle)

Gregory Zajac (8/2012 – 5/2013; Co-Supervised with C.K. Wikle)

Patrick McDermott (8/2011 – 8/2012; Co-Supervised with C.K. Wikle)

Amanda Strickland (8/2011 – 5/2012; Co-Supervised with C.K. Wikle)

TEACHING EXPERIENCE:

STAT 8720 – Intermediate Mathematical Statistics II, University of Missouri, Spring 2016.

STAT 8710 – Intermediate Mathematical Statistics I, University of Missouri, Fall 2015, 2016,2017, 2018.

STAT 4610/7610 – Applied Spatial Statistics, University of Missouri, Spring 2015.

STAT 4780/7870 – Time Series Analysis, University of Missouri, Spring 2005, Fall 2006, 2010,2011, 2013, 2014, 2016, 2017, 2018.

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STAT 1300H – Honors Elementary Statistics, University of Missouri, Fall 2008, Spring 2010,2014.

STAT 9310 – Theory of Linear Models, University of Missouri, Fall 2007, 2008, 2011.

STAT 9100 – Recent Developments in Statistics: Nonparametric Function Estimation, Uni-versity of Missouri, Spring 2009.

STAT 4310/7310 – Sampling Techniques, University of Missouri, Spring 2007.

STAT 9320 – Advanced Theory of Linear Models (Semiparametric Regression), University ofMissouri, Spring 2006.

STAT 4510/7510 – Regression and Correlation Analysis, University of Missouri, Fall 2004,2005.

STAT 301 – Introduction to Biometry, Texas A&M University, Fall 2000.

DEPARTMENTAL AND UNIVERSITY SERVICE:

2018–2021 University of Missouri – A&S Executive Committee

2018–2019 University of Missouri – A&S Undergraduate Research Mentorship ProgramCommittee

2018–2021 Departmental Chair’s Advisory Committee

2018–2019 Departmental Computing Committee

2018– Departmental NISS/SAMSI Representative

2018–2019 Departmental NTT Promotion/Voting Rights Committee

2017–2018 Departmental Search Committee (Committee Chair)

2016–2019 Departmental Ph.D. Exam Committee

2016–2017 University of Missouri – Big Data Faculty Search Committee (Joint Search withCollege of Engineering)

2013–2016 University of Missouri – A&S Executive Committee

2015–2016 Departmental Search Committee

2015–2016 Departmental Applied Masters Program Revision Committee

2015–2016 Departmental Non-Tenure Track Voting Rights Committee

2014– Library Coordinator – Department of Statistics, University of Missouri

2011– University of Missouri – A&S Research Computing Committee

2014–2016 Departmental Chair’s Advisory Committee

2014–2015 Departmental Non-Tenure Track Search Committee

2014–2015 Departmental Tenure-Track Search Committee (Committee Chair)

2014–2015 Departmental Ph.D. Program Revision Committee

2013–2014 Departmental Committee to Review Ph.D. Exams

2013–2014 Departmental Computing Committee

2013–2014 Departmental Online Course Proposal Committee

2012–2015 Departmental Graduate Admissions Committee

2011–2012 Departmental Chair’s Advisory Committee

2011 Departmental Ph.D. Exam Committee (Fall Semester)

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2010–2012 Departmental Search Committee

2010–2011 Departmental Curriculum Review Committee

2010 Departmental Chair Search Committee

2008–2011 Departmental Graduate Admissions Committee (Graduate Recruitment)

2007–2009 Departmental Ph.D. Exam Committee

2007–2008 Departmental Committee to Restructure Ph.D. Exams

2005–2012 Library Coordinator – Department of Statistics, University of Missouri

2004–2006 Recording Secretary – Department of Statistics, University of Missouri

2004–2005 Seminar Chair – Department of Statistics, University of Missouri

Ph.D. AND MASTERS COMMITTEE SERVICE:

Xiaomao Xia (Current), Ph.D., Department of Statistics, Directors: Athanasios Micheas andChristopher Wikle

Sanha Noh (Current), Ph.D., Department of Economics, Director: Christopher Otrok

Yifeng Jia (Current), Ph.D., Department of Economics, Director: Christopher Otrok

Justin Okenye (Current), Ph.D., Department of Statistics, Directors: Athanasios Micheasand Christopher Wikle

Patrick McDermott (2018), Ph.D., Department of Statistics, Director: Christopher Wikle

Ingul Baek (2017), Ph.D., Department of Economics, Director: Christopher Otrok

Ting Wang (2016), Ph.D., Department of Economics, Director: Christopher Otrok

Kuo-Hsuan Chin (2015), Ph.D., Department of Economics, Director: Xiaoguang Ni

Benjamin Goldschmidt (2014), Ph.D., Department of Biomedical Engineering, Director: JohnViator

Rima Dey (2013), Ph.D., Department of Statistics, Director: Athanasios Micheas

Daniel Gladish (2013), Ph.D., Department of Statistics, Director: Christopher Wikle

William Leeds (2012), Ph.D., Department of Statistics, Director: Christopher Wikle

Todd Dewees (2009), Ph.D., Department of Statistics, Director: Athanasios Micheas

Yong Song (2009), Ph.D., Department of Statistics, Director: Christopher Wikle

Jefferey LaCroix (2009), Ph.D., Department of Biomedical Engineering, Director: John Viator

Ali Arab (2007), Ph.D., Department of Statistics, Director: Christopher Wikle

Maria Gabriela Schutte (2007), Ph.D., Department of Finance, Director: Paul Brockman

John Carney (2016), Masters, Department of Statistics, Director: Christopher Wikle

Bridget Whitehead (2016), Masters, Department of Statistics, Director: Christopher Wikle

Christopher Rota (2015), Masters, Department of Statistics, Director: Christopher Wikle

Emilie Shireman (2014), Masters, Department of Statistics, Director: Lori Thombs

Suhwan Lee (2014), Masters, Departments of Statistics and Economics, Co-Directors: Xi-anyang Zhang and David Kaplan

Katy Coffman (2014), Masters, Department of Statistics, Director: Tieming Ji

Richard Shepro (2014), Masters, Department of Statistics, Director: Christopher Wikle

Trevor Oswald (2013), Masters, Department of Statistics, Director: Christopher Wikle

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Feifei Wang (2012), Masters, Department of Statistics, Director: Lori Thombs

Jacob Orme (2012), Masters, Department of Statistics, Director: Christopher Wikle

Christopher Herzog (2011), Masters, Department of Statistics, Director: Christopher Wikle

Xi Luo (2011), Masters, Department of Statistics, Director: Lori Thombs

Haibo Yu (2011), Masters, Departments of Statistics and Economics, Co-Directors: LoriThombs and J. Isaac Miller

Tetyana Beregovska (2010), Masters, Department of Statistics, Director: Christopher Wikle

William Leeds (2009), Masters, Department of Statistics, Director: Christopher Wikle

Andy Ross (2009), Masters, Department of Statistics, Director: Tony Sun

Wensheng Kang (2008), Masters, Department of Statistics, Director: Marco A.R. Ferreira

Siwei Cheng (2008), Masters, Departments of Statistics and Economics, Co-Directors: LoriThombs and Xiaoguang Ni

Ren He (2008), Masters, Department of Statistics, Director: Tony Sun

Robert Talbert (2007), Masters, Department of Biomedical Engineering, Director: John Vi-ator

Isabella Zaniletti (2006), Masters, Department of Statistics, Director: Christopher Wikle

Nur Isa Diandalu (2005), Masters, Departments of Statistics and Economics, Co-Directors:Lori Thombs and Xiaoguang Ni

WORKSHOPS ATTENDED:

Indo-US Workshop on Time Series Analysis – IISER, Pune India, May 2015 (Invited At-tendee).

SAMSI Workshop on Computational Methods for Censuses and Surveys – BLS, January 2014(Invited Attendee).

NISS Affiliates Workshop on Geospatial Methods for Federal Surveys – BLS, September 2013(Invited Attendee).

Analysis of Object Data - SAMSI, September 2010 (Invited Attendee).

Space-time Analysis for Environmental Mapping, Epidemiology and Climate Change -SAMSI, September 2009 (Invited Attendee).

NISS-NASS Cooperative Research Conference – NISS, June 2009 (Invited Attendee).

Environmental Sensor Networks – SAMSI, January 2008 (Invited Attendee).

Modifying Surveys in Response to Disruptions – Sponsored by ASA/BLS/NISS, March 2007(Invited Attendee).

Regression Models for Time Series – SCMA Conference, December 2005.