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Statistics Department Seminar Series: Gerda Claeskens, Professor, Research Centre for Operations Research and Statistics (ORSTAT), KU Leuven

"Most powerful inference after model selection via confidence distributions"
Friday, October 22, 2021
10:00-11:00 AM
Abstract: When a model for a statistical analysis is not given before the analysis, but is the result of a model search endeavor, the uncertainty about the model that is used for inference has consequences for hypothesis testing and for the construction of confidence intervals for the model parameters of interest. Ignoring this uncertainty leads to overoptimistic results, implying that computed p-values are too small and that confidence intervals are too narrow for the intended coverage.
I will explain how to use confidence distributions to obtain valid inference after model selection for the parameters of interest. Under some assumptions, uniformly most powerful post-selection confidence curves are obtained.

This is joint work with Andrea Garcia-Angulo.

Gerda Claeskens is a professor at the Research Centre for Operations Research and Statistics (ORSTAT), KU Leuven. Her research interests are Model selection and model averaging; Post-selection inference; Nonparametric regression.
Building: Off Campus Location
Location: Virtual
Event Link:
Event Type: Workshop / Seminar
Tags: seminar
Source: Happening @ Michigan from Department of Statistics, Department of Statistics Seminar Series