Seminars

Daniela Witten, University of Washington: Selective inference for trees

Monday, November 1, 2021 - 11:00am

Abstract: As datasets grow in size, the focus of data collection has increasingly shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis to generate hypotheses, and then testing those hypotheses on the same data. Unfortunately, this type of 'double dipping' can lead to highly-inflated Type 1 errors. In this talk, I will consider double-dipping on trees. Read more about Daniela Witten, University of Washington: Selective inference for trees

Jae-Kwang Kim, Iowa State University, Weight model approach to Bayesian inference under informative sampling

Monday, October 25, 2021 - 11:00am

Abstract: In probability sampling,   the first-order inclusion probabilities are available for each unit in the sample. If the first-order inclusion probability is correlated with the study variable at hand, even after adjusting for the covariates in the model, then the sampling design becomes informative and the naive analysis ignoring the sampling design can lead to biased estimation. How to handle informative sampling for analytic inference with survey data is an important practical problem. Read more about Jae-Kwang Kim, Iowa State University, Weight model approach to Bayesian inference under informative sampling

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