ncess e-stat quantitative node prof. william browne & prof. jon rasbash university of bristol
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NCeSS e-Stat quantitative node
Prof. William Browne & Prof. Jon Rasbash
University of Bristol
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e-Stat quantitative node
• Linking the ESRC Research Methods and e-Social Science programmes.
• Statistical methodology and software development researchers in Bristol.
• Computer Science (E-Science) researchers at Southampton.
• Other social science researchers at Institute of Education, Manchester and Stirling.
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e-Stat quantitative node
• Node started in September 2009 and is funded for 3 years.
• We aim to produce software tools to cater for three types of user: novice practitioners, advanced practitioners and statistical algorithm developers
• Capacity building and methodology development require iterative interaction between all three groups
• We are currently in the planning stage merging the skills of the various contributors.
• We are however building on many strengths as detailed in the following slides.
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MLwiN software package
• Statistics package for multilevel modelling.
• Development supported by ESRC for many years.
• Over 7,000 copies sold worldwide (free to UK academics)
• Cited in 1,500+ ISI journal articles
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MCMC in MLwiN
• Computationally – intensive Markov Chain Monte Carlo Simulation based methods
• Statistical model estimates obtained by generated lots of random number draws from the posterior distribution.
• Can benefit from parallelisation in many ways.
MCMC algorithm converges on a distribution. Parameter estimates and intervals are then calculated from the simulation chains.
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Sample Size calculations via simulation
• Original work funded in a recently finished ESRC grant.
• Is a computationally intensive method as involves running same model on many datasets and assessing how often a confidence interval contains 0
• Ideal candidate for
parallelisation.
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My experiment project (Universities of Southampton and Manchester)
Social Networking tool for sharing scientific artifacts
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Component wise approach
Also interested in interoperability between statistical software packages so components may be existing packages
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Prototype for Algebraic Processing Component
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Software Component for code generation
• Algebraic processing component outputs statistical distributions (posteriors) in MATHML
• MATHML files taken as input into Python.• Python code creates C++ code for running the
model for 1 iteration. • C++ code compiled to a Python function.• Function can be called within Python.• C++ code can be viewed and edited by expert
users.
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Specific areas of application in the social sciences
• Measuring segregation – proposal for complex modelling
• ESDS feasibility study project: Changing circumstances during childhood
• Social Networks in Multilevel structures
• Handling missing data via multiple imputation
• Sample size calculations
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Integration into workbooks
• Model Specification and Estimation software components – freestanding Python application(s) operated by own GUI or script.
• These components along with tables, graphs, equations and diagrams can be incorporated in SAGE executable books.
• myExperiment will then act as a searchable repository for locating and sharing executable books