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RIDIR: Collaborative Research: Bayesian analytical tools to improve survey estimates for subpopulations and small areas

In this project, a set of tools will be built for in-depth analysis of survey data, making use of and extending statistical methods for estimation for small subgroups. Classical methods for surveys are focused on aggregate population-level estimates but we can learn much more using small-area estimation. The goal of this project is to build a user-accessible platform for modeling and visualizing survey data that would give estimates for arbitrary subgroups of the population, along with visualization tools to display estimates of interest.

Benjamin Goodrich

Associate Research Scholar; Lecturer in the Department of Political Science

CI-SUSTAIN: Stan for the Long Run

Stan is a software package that transforms scientific discovery by allowing scientists to quickly and easily explore, evaluate, and refine rich scientific hypotheses tailored to their particular research question and data collection mechanism. For computational reasons, analyses of data (big or otherwise) have tended to be simple and focused more on the difficulties of manipulating the data than on realistic scientific models.

Regina Dolgoarshinnykh

Associate Research Scientist in the Department of Statistics; Adjunct Assistant Professor of Statistics

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