mag. rer. nat., dr. rer - alpen-adria-universität klagenfurt · 11.spöck, g.: spatial sampling...

GunterSpöck

Data26.06.1966 Birth.

Nationality: Austria.Status: married.

EducationOctober 2011 Habilitation in Statistics, at the University of Klagenfurt, Thesis Title: The applica-

tion of Bayesian statistics and convex design methodologies to geostatistical predictionand sampling design.

2003–2005 Dr.rer.nat, Department of Statistics, University Klagenfurt, Supervisors: Prof. Jür-gen Pilz and Prof. Werner Müller (University Linz), Thesis Title: Bayesian spatialprediction and sampling design.

1987–1997 Mag.rer.nat, Department of Mathematics, University Klagenfurt, Supervisor: Prof.Jürgen Pilz, Thesis Title: Die geostatistische Berücksichtigung von a priori Ken-ntnissen über die Trend- und die Kovarianzfunktion aus Bayesscher- Minimax- undSpektraler Sicht.

1977–1985 Bundesgymnasium St.Veit/Glan.

Experiencesince March

2012Associate Professor, Department of Statistics, University Klagenfurt.

2005–2012 Assistant Professor, Department of Statistics, University Klagenfurt.2005 Project Assistant, Department of Mathematics, University Klagenfurt, Project Title:

Quantum Cryptography SECOQC.2003–2005 Project Assistant, Department of Mathematics, University Klagenfurt.

Project title: Optimal Design for Correlated Processes and its Role in Spatial Statistics1999–2002 Statistician, CTR Carinthian Tech Research, Villach.

Developement of a Statistics Toolbox for Hyperspectral Imaging and Chemometrics

Courses held at University KlagenfurtWS2018/2019 Generalized Linear Models.

Universitätsstrasse 65-67 – 9020 KlagenfurtH +43 650 2606166 • T +43 2700 3125 • B [email protected]

mailto:[email protected]

SS2018 Statistical Methods and Data Analysis, Computational Statistics, StochasticProcesses 2 (Stochastic Differential Equations), Practicum.

WS2017/2018 Linear Modells, Stochastic Processes 1, Statistical Decision Theory.SS2017 Seminar with Bachelorthesis, Computational Statistics, Praktikum Technical

Mathematics, Bayesian Statistics, DK Seminar of the Karl Popper College.WS2016/2017 Linear Modells, Stochastic Processes 1, DK Seminar of the Karl Popper Col-

lege.SS2016 Computational Statistics, Statistical Decision Theory, Stochastic Processes 2

(Stochastic Differential Equations), Seminar with Bachelorthesis.WS2015/2016 Linear Modells, Selected Topics from Stochastic Processes (Stochastic Differ-

ential Equations), Seminar with Bachelorthesis, Seminar in Statistics.SS2015 Estimation and Test Theory, Computational Statistics, Statistical Decision

Theory, Seminar with Bachelorthesis.WS2014/2015 Statistical Methods and Data Analysis, Selected Topics from Stochastic Pro-

cesses (Stochastic Differential Equations).SS2014 Estimation and Test Theory, Statistical Decision Theory, Seminar in Statistics,

Seminar with Bachelorthesis.WS2013/2014 Statistical Methods and Data Analysis, Stochastic Processes 2 (Stochastic

Differential Equations), Proseminar in Applied Statistics.SS2013 Statistical Decision Theory, Multivariate Analysis.

WS2012/2013 Computational Statistics II, Classification and Pattern Recognition.SS2012 Statistical Decision Theory, Multivariate Analysis.

WS2011/2012 Spatial Statistics, Stochastic Processes.SS2011 Time Series, Classification and Pattern Recognition.

WS2010/2011 Linear and Nonlinear Regression Analysis, Stochastic Processes.SS2010 Time Series, Statistical Decision Theory.

WS2009/2010 Stochastic Processes, Classification and Pattern Recognition.SS2009 Computational Statistics, Statistical Decision Theory.

WS2008/2009 Statistical Experimental Design, Statistical Methods and Data Analysis II.SS2008 Computational Statistics, Classification and Pattern Recognition .

WS2007/2008 Linear and Nonlinear Regression Analysis, Statistical Methods and Data Anal-ysis II.

SS2007 Stochastics II, Computational Statistics, Multivariate Data Analysis.WS2006/2007 Linear and Nonlinear Regression Analysis, Spatial Statistics.

SS2006 Stochastics II, Computational Statistics, Multivariate Data Analysis.WS2005/2006 Spatial Statistics, Linear and Nonlinear Regression Analysis .WS2004/2005 Repetitorium to Linear Algebra and Geometry II .

Course held at University of Natural Resources and Life Sciences,Vienna

SS2013 Spatial Statistics.



Bachelor Theses, Master Theses and Dissertations

Bachelor Theses .

SS2010 Petra Grün: Monte-Carlo-Simulation und Reversible-Jump-Algorithmus.

SS2012 Thomas Graf: Implementierung von Trans-Gauss-Kriging in R.

S2012 Christina Terbul: Optimale statistische Versuchsplanung im Bayesschen Lin-earen Regressionsmodell.

SS2012 Andreas Lembacher: MCMC-Methoden in der Zeitreihenanalyse.

SS2012 Thomas Wieser, SS2012: Multivariate Finanzzeitreihenanalyse.

SS2014 Stefan Schrunner: Bestimmung der First-Passage-Time-Verteilung vonDegradation-Path-Modellen.

SS2016 Jasmin Wachter: Proportional Hazards Models for Exact and Right-CensoredTime-to Event Data.

WS2016/17 Eva Maria Krassnitzer: Ermittlung des Liquidity of Risk mittels der Peak overThreshold Methode.

WS2017/18 Daniela Sternig: Klassifikations- und Regressionsmethoden zur Analyse desAuftretens von Asthma (Klassifikation).

WS2018/19 Arijana Medanovic: Klassifikations- und Regressionsmethoden zur Analyse desAuftretens von Asthma (Regression).

Master Theses .

SS2013 Anja Zernig: Optimal Design of Experiments for Semiconductor Data followinga Mixtures-of-Experts Model.

SS2014 Thomas Graf: Vine Copulas und deren Anwendung in der Finanzmathematik.

SS2014 Andreas Lembacher: Data Mining for Financial Risk Management.

SS2014 Milan Zivadinovic: Multivariate Datenanalyse von hyperspektralen Bilddaten.

WS2014/15 Thomas Wieser: Autonomous Task Selection of Unmanned Aerial Vehicles forDelivery of Goods: Partitioning versus Non-partitioning Policies.

WS2015/16 Marina Jury: Qualität von Verfahren zur Schadenreservierung.

SS2016 Tanja Maier: Räumliche statistische Analyse und Messnetzplanung für HCB-und Hg-Daten aus dem Görtschitztal.



WS2017/18 Maximilian Arbeiter: Modellierung von Schadstoffdispersion anhand eines dy-namischen Discountingmodells und eines stochastischen Partikelmodells.

ongoing Daniela Sternig: Value at Risk.

Dissertations .

WS2012/13 Muhammad Mohsin, Second Reviewer: A New Class of Distributions Gener-ated from Functional Scale Parameter.

WS2018/19 Ioannis Spyroglou, Second Reviewer: Modeling and prediction of cross-sectional and longitudinal Biomedical data using Bayesian methods and gen-eralized linear models.

ongoing Corinna Kofler: Automatische Klassifizierung von Defekten auf Wafern.

ongoing Tanja Maier: Modelling of Person Behaviour through Machine Learning andMarkov Chains: Individualized Response Systems for Independently living El-derly People.

ongoing Maximilian Arbeiter: Simulation of the Behavior of Elderly Persons in theirLiving Environment using Decision Theory.

Academic Rewards2005 PhD with distinction.1997 Reward from the Austrian Statistical Society for the best Master/PhD Thesis

in Mathematical Statistics.1997 Master with distinction.

Aquired Projects2016 3-year FFG Project "Virtuelle Indikationen und Pflegestrukturen" together

with P.SYS, Villach. Task: Developement of a self-learnig tool for the supportof elder people..

2016 3-year project about "Defect Classification on Wafers" financed by InfineonVillach .

2016 5-year project about "Planung und Instandhaltung eines Sensormessnetzes fürUmweltdaten für das Görtschitztal".

2016 5-year Book-Project: "Stochastic (Partial) Differential Equations: Simulation,Estimation, Filtering, Control, Monitoring. An Approach for the Practioner".

2016 4.5 years project about "Analyse von Blut- und Harnproben und Bestandsauf-nahme des Gesundheitszustandes der Bevölkerung des Görtschitztales".

2015 1-year project about "Die Beurteilung von Fichtennadel- u. Bodenproben ausdem Görtschitztal nach dem HCB-Skandal".

2015 6-years project about "Pollution Dispersion Modeling".



Current Intentions and Preparations2017-2018 Build-up of a Christian-Doppler Lab for "Environmental Monitoring and Risk

Evaluation" at the Department of Statistics..2017-2018 Application for a 3-years, 2-person FWF-project on "Pollutant Dispersion, -

Simulation and Implementation"..

Research InterestsStatistics Applied Statistics, Bayesian Statistics, Bayesian Decision Theory, AI and

Pattern Recognition, Experimental Design, Spatial Sampling Design, SpatialStatistics, Environmental Statistics, Epidemiology and Health Risk Evaluation,Environmental Monitoring, Pollution Dispersion Modeling, Stochastic Differ-ential Equations.

Physics andChemistry

Environmental Sensors, Atmospheric Physics and Chemistry, SoilPhysics/Chemistry and Hydrology/Water Chemistry, Meteorology, As-tronomy.

LanguagesGerman native languageEnglish perfectFrench average

Computer skillsProgramming

Languages:C++, CUDA, Matlab, Octave, R, Mathematica, HTML, Latex.

OperatingSystems:

Linux, Windows, MacOS.

DevelopedSoftware:

Matlab Toolbox for Hyperspectral Imaging and Chemometrics, spatDesign: AMatlab and Octave Toolbox for Spatial Sampling Design and Geostatistics.

Functions2014/15 Member of the appointment committee "Stochastische Prozesse".

Publications

Book Chapters1. Spöck G: Stochastic (Partial) Differential Equations: Estimation, Simulation, Filtering, Control. An

approach for the practioner. Book forthcoming 2020.

2. Spöck G., Pilz J.: Spatial sampling design by means of spectral approximations to the error process,



In: J. Mateu and W.G. Müller (Eds.), Spatio-temporal design: Advances in efficient data acquisition,Chichester, UK: Wiley, 2013, pp. 72-102.

3. Spöck, G.: Spatial Sampling Design and Soil Science, In: E. Burcu Ozkaraova Gungor (Ed.), Principles,Application and Assessment in Soil Science, INTECH, Croatia, 2011, pp. 257-278.

4. Spöck G., Pilz J.: Analysis of areal and spatial interaction data. In: Lovric Miodrag (Ed.): InternationalEnzyclopedia of Statistical Science. Berlin, Heidelberg, New York: Springer Verlag GmbH, 2011, pp.6

Conference Proceedings (peer-reviewed)1. Kofler C., Muhr R. and Spöck G.: Detecting Star Cracks in Topography Images of Specular Back

Surfaces of Structured Wafers, 2018 17th IEEE International Conference on Machine Learning andApplications (ICMLA), 2018, p. 406-412.

2. Kofler C., Spöck G., Muhr R.: Classifying Defects in Topography Images of Silicon Wafers, Proceed-ings of the 2017 Winter Simulation Conference, W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N.Mustafee, G. Wainer, and E. Page, eds., 2017, 12 pp.

3. Zernig A., Bluder O., Spöck G.: Optimal design of experiments for semiconductor lifetime data, Pro-ceeding of the 9th International Conference on Ph.D. Research in Microelectronics and Electronics(PRIME), 2013, p. 285-288,

4. Spöck G.: Spatial sampling design with skew distributions: The special case of trans-Gaussian kriging.Proceedings of Ninth International Geostatistics Congress, Oslo, 2012, pp. 21.

5. Spöck G.: Bayesian locally stationary trans-Gaussian kriging using generalized Voronoi tesselations. In:D. Cornford, G. Dubois, D. Hristopoulos, E. Pebesma, J. Pilz (Ed.): Proceedings of StatGIS 2009 /CD. Chania: Technical University of Crete, 2009, 6 pp.

6. Spöck G., Kazianka H., Pilz J.: Modeling and interpolation of non-Gaussian spatial data: a compar-ative study. In: D. Cornford, G. Dubois, D. Hristopoulos, E. Pebesma, J. Pilz (Ed.): Proceedings ofStatGIS 2009 / CD. Chania: Technical University of Crete, 2009, 6 pp.

7. Spöck G., Kazianka H., Pilz J.: Bayesian trans-Gaussian kriging with log-log- transformed skew data.In: J. Pilz (Ed.): Interfacing Geostatistics and GIS. Berlin, Heidelberg, New York: Springer VerlagGmbH, 2009, pp. 29-44.



8. Pilz J., Kazianka H., Spöck G.: Interoperability - Spatial Interpolation and Automated Mapping. In: T.Tsiligiridis (Ed.): Proceedings of the 4th International Conference on Information & Communication,Technologies in Bio & Earth Sciences. Athens: Agricultural University of Athens, 2008, pp. 110-118.

9. Pilz J., Spöck G.: Bayesian Spatial Sampling Design. In: J.M. Ortiz, X. Emery (Ed.): Geostats 2008.Santiago: Gecamin Ltd, 2008, pp. 21-30.

10. Spöck G., Pilz J.: Non-Stationary Spatial Modeling using Harmonic Analysis. In: J. M. Ortiz, X.Emery (Ed.): Geostats 2008. Santiago: Gecamin Ltd, 2008, pp. 389-398.

11. Pilz J., Spöck G.: Spatial sampling design for prediction taking account of uncertain covariance struc-ture. In: M. Caetano, M. Painho (Ed.): Proceeding of Accuracy 2006. Lisboa: Instituto GeográficoPortuguês (IGP), 2006, S. 109-118.

12. Pilz J., Pluch Ph., Spöck G.: Bayesian Kriging with lognormal data and uncertain variogram param-eters. In: P. Renard, H. Demougeot-Renard, R. Froidevaux (Ed.): Geostatistics for EnvironmentalApplications. Berlin: Springer, 2005, pp.51-62.

13. Pilz J., Helgason Th., Hofer V., Spöck G.: Wavelet-based classification of aggregates using MIR andNIR spectra. In: R. Leitner (Ed.): 2nd International Spectral Imaging Workshop. Wien: Österreichis-che Computer Gesellschaft ÖCG, 2005, S. 11-18.

14. Gurschler C., Serafino G., Spöck G., Del Bianco A., Kraft M., Kulcke A.: Spectral Imaging for Classifi-cation of Natural and Artifical Turquoise Samples. In: OPTO 2002 (Ed.): OPTO 2002. Erfurt: 2002,10.

15. Pilz J., Schimek M.G., Spöck G.: Taking account of uncertainty in spatial covariance estimation. In:E.Y. Baafi (Ed.): Geostatistics Wollongong. Dordrecht: Kluwer Academic Publishers, 1997, 302-313.

Journal Articles1. Khan F., Spöck G. and Pilz J.: A novel Approach for Modelling Pattern and Spatial Dependence

Structures between Climate Variables by Combining Mixture Models with Copula Models, to appear inInternational Journal of Climatology, 2019.

2. Kofler C., Spöck G. and Muhr R.: Detecting Star Cracks in Image Stacks of Specular Silicon WaferBack Surface Regions: Performance comparison of CNNs and SVMs, to appear in Sensors, 2019.



3. Spyroglou I.I., Spöck G., Rigas A.G. and Paraskakis E.N.: Evaluation of Bayesian classifiers in asthmaexacerbation prediction after medication discontinuation. BMC Res. Notes. Vol. 11, 05.08.2018, 1-6.

4. Spyroglou I.I. Spöck G., Rigas A.G. and Paraskakis E.N.: Asthma exacerbation prediction for patientswith repeated measurements using Generalized Linear Models, forthcoming.

5. Spyroglou I.I., Spöck G., Chatzimichail E.A., Rigas A.G., Paraskakis E.N.: A Bayesian Logistic Regres-sion approach in Asthma Persistence Prediction, Epidemiology Biostatistics and Public Health, Vol.15, N. 1, 2018.

6. Zahid E., Hussain I., Spöck G., Hussain T.: Spatial Prediction and Optimized Sampling Design forSodium Concentration in Groundwater, PLOS ONE, Vol 11, 2016, p. 1-16.

7. Spöck G. and Pilz J.: Incorporating covariance estimation uncertainty in spatial sampling design forprediction with trans-Gaussian random fields, Front Environ Sci 3:39, 2015, pp. 22.

8. Mohsin M., Pilz J. and Spöck G.: A new bivariate Gamma distribution generated from functional scaleparameter with application to drought data. Stoch Environ Res Risk Assess, 27(5), 2013, 1039-1054.

9. Mohsin M., Spöck G. and Pilz J.: On the performance of a new bivariate pseudo Pareto distributionwith application to drought data. Stoch Environ Res Risk Assess, 26(7), 2012, 925-945.

10. Hussain I., Spöck G., Pilz J., Faisal M. and Yu H.-L.: Spatio-temporal interpolation of precipitationincluding covariates: during monsoon periods in Pakistan. Pakistan Journal of Statistics, 28, 2012,351-365.

11. Spöck, G.: Spatial sampling design based on spectral approximations to the random field, Environ-mental Modelling and Software, 33, 2012, pp. 48-60.

12. Spöck G., Hussain I.: Spatial sampling design based on convex design ideas and using external driftvariables for a rainfall monitoring network in Pakistan. Statistical Methodology, Vol. 9, 2012, pp.195-210.

13. Pilz J., Kazianka H., Spöck G.: Some advances in Bayesian spatial prediction and sampling design. In:Spatial Statistics, Amsterdam: Elsevier B.V 2012, pp. 65-81.



14. Mohsin M., Pilz J., Spöck G., Ahsanullah M.: On Some New Bivariate Pseudo Gamma Distributions,Journal of Applied Statistical Science, 18(2), 2011, pp. 239-250.

15. Hussain I., Pilz J., Spöck G.: Homogeneous climate regions in Pakistan. In: International Journal ofGlobal Warming, Vol. 3, No.1/2, 2011, pp. 55 - 66.

16. Hussain I., Pilz J. and Spoeck G.: Reply to Comment on “Hierarchical Bayesian space-time interpo-lation versus spatio-temporal BME approach”. In: Advances in Geosciences, Göttingen: CopernicusPublications 25, 2010, 181 pp.

17. Hussain I., Pilz J., Spöck G.: Hierarchical Bayesian space-time interpolation versus spatio-temporalBME approach. In: Advances in Geosciences, Vol. 25, Göttingen: Copernicus Publications 2010, pp.97-102.

18. Hussain I., Spöck G., Pilz J., Yu H.-L.: Spatio-temporal interpolation of precipitation during monsoonperiods in Pakistan. In: Advances in Water Resources, New York: Elsevier Science Inc, 33, 2010, pp.880-886.

19. Spöck G., Pilz J.: Spatial Sampling design and Covariance-Robust Minimax Prediction based on Con-vex Design Ideas. In: Stochastic Environmental Research and Risk Assessment, Berlin, Heidelberg,New York: Springer Verlag GmbH 24, 2010, 3, pp. 463-482.

20. Pilz J., Spöck G.: Why do we need and how should we implement Bayesian kriging methods. In:Stochastic Environmental Research and Risk Assessment, Berlin, Heidelberg, New York: Springer Ver-lag GmbH 22, 2008, 5, pp. 621-632.

21. Del Bianco A., Serafino G., Spöck G.: An introduction to spectral imaging. In: Useful and AdvancedInformation in the Field of Near Infrared Spectroscopy, 37, 2003, 661(2), 55-75.

22. Kulcke A., Gurschler C., Spöck G., Leitner R., Kraft M.: On-line classification of Synthetic Polymersusing Near-Infrared Spectral-Imaging. In: Journal of Near Infrared Spectroscopy, 2003, 11, 71-81.

Workshop Articles1. Spöck G.: Bayesian approaches to kriging of non-stationary and skewed data, Statgis 2009, Milos

Island, 19. June 2009, pp. 68

2. Spöck G. : Bayessche räumliche Statistik und Computersimulationsexperimente, SIMNET-Summerschool,10. September 2010, pp.73



Research Reports1. Maier T., Spöck G.: Bericht zur Darstellung der Belastung der Görtschitztaler Bevölkerung mit Hex-

achlorbenzol und toxischen Metallen. Eine explorative Datenanalyse von Blut- und Harn-Messungen.Institut für Statistik, Alpen-Adria Universität Klagenfurt, 1. März 2017, pp. 112.

2. Pilz J., Spöck G.: Bayesian Trans-Gaussian Kriging. Münster: Westfälische Wilhelms-Universität Mün-ster, 2007, 37.

3. Spöck G.: Smile-Face-Correction and Wavelength-Calibration. Villach: CTR AG, Technical Report,2002.

4. Spöck G.: Unsupervised Classification. Villach: CTR Carinthian Tech Research AG, 2001.

5. Spöck G., Del Bianco A.: Multivariate Image Analysis. Villach: CTR AG, Technical Report, 2000.

6. Spöck G., Del Bianco A.: Multivariate Calibration. Villach: CTR AG, Technical Report, 2000.

7. Spöck G., Del Bianco A.: Pre-Processing. Villach: CTR AG, Technical Report, 2000.

8. Spöck G., Del Bianco A.: Supervised Pattern Recognition. Villach: CTR AG, Technical Report, 2000.

Other scientific publications1. Spöck G.: The application of Bayesian statistics and convex design methodologies to geostatistical

prediction and sampling design. Habilitation Thesis. Klagenfurt: Alpen-Adria Universität, 2011, 127pp.

2. Spöck G.: Bayesian Spatial Prediction and Sampling Design. Dissertation. Klagenfurt: Alpen-Adria-Universität Klagenfurt, 2005, 183 pp.

3. Spöck, G.: Die geostatistische Berücksichtigung von A-Priori-Kenntnissen über die Trendfunktion unddie Kovarianzfunktion aus Bayesscher- Minimax- und Spektraler-Sicht. Master Thesis. Klagenfurt:Alpen-Adria-Universität Klagenfurt, 1997, 84 pp.



Presentations

Invited Speaker1. Spöck G.: The fusion of stationary and mobile environmental sensors and pollutant dispersion cal-

culations with respect to the problematic of sampling design. Seminar of the Dept. of ElectricalEngineering, Democritos University of Xanthi, Greece, 05.11.2018.

2. Spöck G.: Ein Blick in die Zukunft: Die Fusion von mobilen und nicht-mobilen Umweltsensormessnet-zen, Schadstoffausbreitungsrechnung und LIDAR. Symposium: Aspekte industrieller Umweltprobleme,Uni Salzburg, Österreich, 18.05.2018.

3. Spöck G., Tanja Maier: Die HCB- und Hg-Belastung im Görtschitztal. Hofgespräch von Rettet dasGörtschitztal, 09.12.2016, 9372 Hochfeistritz.

4. Spöck G.: A dynamic discounting model for the dispersion and redistribution of pollutants emitted frompoint sources in air, surface, subsurface and groundwater. Open Salzburg Mathematics Colloquium,3. March 2016.

5. Spöck G.: Dynamische Modellierung von Schadstoffemissionen aus Punktquellen zur Bestimmung vonLuft-, Bodenoberfläche-, Untergrund- und Grundwasserverschmutzungen. Wien-Ausstellung von Ret-tet das Görtschitztal, 24.10.2015.

6. Spöck G.: Incorporating covariance estimation uncertainty in spatial sampling design for predictionwith trans-Gaussian random fields. Karl Popper Doktoratskollege, 10.06.2015, Klagenfurt.

7. Spöck G.: Statistische Aspekte zur HCB-Belastung im Görtschitztal und Ideen zur Erstellung einerBelastungskarte. Stammtisch Initiative Zukunft Görtschitztal, 19.03.2015, 9372 Eberstein .

8. Spöck G.: Spatial sampling design with skew distributions: The special case of trans-Gaussian kriging,Seminar, Department of Ecology, TexasTech University, August, 2012.

9. Spöck G.: To be or not to be Bayesian. Habilitationskolloquium, Universität Klagenfurt, 25. October2011.

10. Spöck G.: Bayessche räumliche Statistik und Computersimulationsexperimente. SIMNET Summer-school, Graz, 10. September 2010, http://www.simnet-styria.at/index.php/veranstaltungen/details/29-



summer-school-simnet.

11. Pilz J., Spöck G.: Bayesian Spatial Sampling Design. SAMSI Geostats Workgroup session, SAMSINorth Carolina, 18. November 2009.

12. Spöck G., Pilz J.: Perspectives on optimum spatial sampling design. SAMSI Geostats Workgroupsession, SAMSI North Carolina, 18. November 2009.

13. Spöck G., Pilz J.: Non-stationary spectral representations, GLMMs and spatial sampling design. SAMSINon-non workgroup session, North Carolina, 11. November 2009.

14. Spöck G., Kazianka H., Pilz J.: Bayesian spatial prediction. StatGIS 2009, Milos Island, 19. June2009, www.math.uni-klu.ac.at/stat/Tagungen/statgis/2009.

15. Spöck G.: Minimax and Bayesian approaches taking into account the uncertainty of the covariancefunction in kriging prediction and spatial sampling design. Forschungsseminar des Institus für Statistik,JKU Linz, Linz, 9. November 2006, www.math-uni-klu.ac.at/stat/ guspoeck/linz06.pdf.

16. Pilz J., Helgason Th., Hofer V., Spöck G.: Wavelet-based Classification of Aggregates using MIR andNIR Spectra. 2nd International Spectral Imaging Workshop, Villach, 19. September 2005.

Selected Speaker1. Spöck G.: Die Fusion von stationären und mobilen Umweltsensormessnetzen, Schadstoffausbreitungsrech-

nung und LIDAR und die Problematik des Sampling Design. Statistische Woche 2018, Linz, Österreich,11.09.2018.

2. Spöck G.: Dynamic modelling of pollutant emissions from point sources for the determination of air-,surface-, subsurface- and groundwater pollution. IWS 2015: 8th International Workshop on Simula-tion, 23.09.2015, Wien.

3. Spöck G.: Dynamic modelling of pollutant emissions from point sources for the determination of air-,surface-, subsurface- and groundwater pollution. IAMG 2015 The 17th annual conference of the Inter-national Association for Mathematical Geoscience, 10.09.2015, Freiberg.

4. Spöck G.: Taking account of covariance estimation uncertainty in spatial sampling design for trans-Gaussian random fields. The 2015 International Conference on Environmental Science and Geoscience,17.03.2015, Wien.



5. Spöck G., Pilz J.: Taking account of covariance estimation uncertainty in spatial sampling design forprediction with trans-Gaussian random fields, 12th Workshop on Stochastic Models and their applica-tion, Wrocław, Poland, 16-20 February 2015.

6. Spöck G.: Spatial sampling design for random fields having skewed distributions: The case of trans-Gaussian Kriging. Optimal Design of Experiments Theory and Application, Vienna, 28. September2011.

7. Spöck G.: Simplifying objective functions and avoiding stochastic search algorithms in spatial samplingdesign. JSM 2010, Vancouver, 2. August 2010.

8. Spöck G.: Bayesian locally stationary trans-Gaussian kriging using generalized Voronoi tesselations.StatGIS 2009, Milos Island, 18. Juni 2009, www.math.uni-klu.ac.at/stat/Tagungen/statgis/2009.

9. Pilz J., Spöck, G.: Bayesian Spatial Sampling Design. Geostats 2008, Santiago Chile, 2. Dezember2008.

10. Spöck G.: Non-Stationary Spatial Modeling using Harmonic Analysis. Geostats 2008, Santiago Chile,2. Dezember 2008.

11. Spöck G.,: Spatial covariance-robust minimax prediction based on experimental design ideas, MODA8,Almagro, Spain, June 4-8, 2007.

12. Spöck G.: Bayesian trans-Gaussian Kriging and the Uncertainty of Variogram Estimates. StatGIS 2007,Klagenfurt, 25. September 2007.

13. Spöck G.: Spatial Covariance-Robust Minimax Prediction based on Experimental Design Ideas. Moda8,Almagro Spanien, 6. Juni 2007.

14. Spöck G.: Noninformative Priors with special Application to Spatial Prediction. 32nd Annual SpringLecture Series, Spatial and Spatio-Temporal Statistics, Fayetteville, Arkansas USA, 14. April 2007.

15. Spöck G., Pilz J.: Spatial sampling design for prediction taking account of uncertain covariance struc-ture. Spatial Accuracy 2006, Lisboa, 5. Juli 2006.



16. Spöck G., Pilz J.: Transgaussian Bayesian Kriging. Österreichische Statistik Tage, Klagenfurt, 20.Oktober 2005.

17. Pilz J., Spöck G.: Bayesian Kriging with lognormal data and uncertain variogram parameters. FifthEuropean Conference on Geostatistics for Environmental Applications, Neuchâtel, 15. Oktober 2004.

18. Pluch Ph., Spöck G., Pilz J.: Taking into account uncertainty in spatial covariance estimation forBayesian prediction. useR2004, Wien, 21. Mai 2004.

19. Leitner R., Kulcke A., Spöck G., Gurschler C.: Real-time classification of Polymers with NIR SpectralImaging. AGM 2002 / OCG, Workshop of the Austrian Society for Pattern Recognitions, 2002.

20. Serafino G., Spöck G.: Spectral Imaging for Classification of Natural and Artifical Turquoise Samples.Conf. OPTO 2002, Erfurt, 2002.

21. Pilz J., Spöck G.: Taking Account of Uncertainty in Spatial Covariance Estimation. 5th Int. Geost.Congr, Wollongong Australia, 1997.

Poster Presentations1. Arbeiter M., Scholte Van Mast B., McClatchie S., Spöck G., Maier T.: Simulation of the Behavior

of Elderly Persons in their Living Environment using Decision Theory. Statistische-Woche, September2018, Linz.

2. Spöck G.: A dynamic discounting model for the dispersion and redistribution of pollutants emittedfrom point sources in air, surface, subsurface and groundwater. ÖSG-Tage, 23.10.2015, Wien.

3. Spöck G., Gebhardt A.: Implementing GPU computations in Octave and statistical applications. ÖSG-Tage, 23.10.2015, Wien.

4. Spoeck, G.: Spatial sampling design with skew distributions: the special case of trans-Gaussian kriging.GeoStats 2012, Oslo, 12. June 2012.

5. Spöck G., Kazianka H., Pilz J.: Objective Bayesian analysis of spatially correlated data including mea-surement error. Ninth Valencia International Meeting on Bayesian Statistics, Benidorm, 5. June 2010.



6. Kazianka H., Spöck G., Pilz J.: Trans-Gaussian and Copula-based Bayesian Kriging: A Comparison.Opening Workshop: SAMSI Program on Space-time Analysis for Environmental Mapping, Epidemiol-ogy and Climate Change, North Carolina, 15. September 2009.

7. Hussain I., Spöck G., Pilz J.: Homogeneous Climate Regions in Pakistan. Proceedings of The GlobalConference on Global Warming - 2009, Istanbul, 6. July 2009.

8. Spöck G., Kazianka H., Pilz J.: Modeling and interpolation of non-Gaussian spatial data: A comparativestudy. StatGIS 2009, Milos Island, 18. June 2009, www.math.uni-klu.ac.at/stat/Tagungen/statgis/2009.

9. Spöck G.: Bayesian locally stationary trans-Gaussian kriging using generalised Voronoi tessellations.GEOENV 2008, Southampton, 9. September 2008.

10. Pilz J., Gebhardt A., Kazianka H., Müller H., Spöck G.: Wie reagiert man am besten auf Katastrophenwie Tschernobyl. Tag der Forschung, Klagenfurt, 16. November 2007.

11. Pilz J., Pluch Ph., Spöck G.: Bayesian Transgaussian Kriging and Spatial Sampling Design. Int. Conf.of European Geophysical Union (EGU), Wien, 7. April 2006.

12. Pilz J., Spöck G., Pluch Ph.: A Bayesian way out of the dilemma of underestimation of predic-tion errors in Kriging. Séminaire Européen de Statistique 2004, Bernried, 15. December 2004,http://www.stat.uni-muenchen.de/semstat2004/.

Reviewer for the following Scientific Journals and Conferences1. EURAM (European Academy of Management) 20172. EURAM 20123. EURAM 20094. STATGIS 20095. STATGIS 20076. Journal of Water Management 20157. Computers and Geosciences 20108. Journal of Stochastic Environmental Research & Risk Assessment 2011, 2011, 20149. Computational Statistics and Data Analysis 2011

10. Environmental Modelling and Software 201211. Mathematical Geosciences 2015



12. Technometrics 201513. Journal of Applied Statistics 2014, 201614. Computational Statistics & Data Analysis 201115. Mathematical Geology 201416. Frontiers in Environmental Science 2017, 201917. Statistical Papers 2018



mag. rer. nat., dr. rer - alpen-adria-universität klagenfurt · 11.spöck, g.: spatial sampling...

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