Event Date:
Friday, May 16, 2025 - 3:30pm to 4:45pm
Event Date Details:
Friday May 16th, 2025
Event Location:
- Henley Hall 1010
Event Price:
FREE
Event Contact:
Prof. Daneila Witten
- Annual Sobel Lecture
Annual Sobel Lecture, with Daniela Witten:
Established in 2004, this lecture has been a preeminent lecture of distinction in the Department of Statistics and Applied Probability at UCSB. The lecture was established in recognition of the contributions made to statistical sciences by Prof. Milton Sobel 1919-2002.
Abstract:
Suppose that a data analyst wishes to report the results of a least squares linear regression only if the overall null hypothesis—namely, that all non-intercept coefficients equal zero—is rejected. This practice, which we refer to as F-screening (since the overall null hypothesis is typically tested using an F -statistic), is in fact common practice across a number of applied fields. Unfortunately, it poses a problem: standard guarantees for the inferential outputs of linear regression, such as Type 1 error control of hypothesis tests and nominal coverage of confidence intervals, hold unconditionally, but fail to hold conditional on rejection of the overall null hypothesis.
In this talk, I will present an inferential toolbox for the coefficients in a least squares model that are valid conditional on rejection of the overall null hypothesis. I will present selective p-values that lead to tests that control the selective Type 1 error, i.e., the Type 1 error conditional on having rejected the overall null hypothesis. Furthermore, they can be computed without access to the raw data, using only the standard outputs of a least squares linear regression, and therefore are suitable for use in a retrospective analysis of a published study. I will also present confidence intervals that attain nominal selective coverage, and point estimates that account for having rejected the overall null hypothesis.
I will illustrate this selective procedure via re-analysis of a published result in the biomedical literature, for which the raw data is not available.
This is joint work with Olivia McGough (U. Washington) and Daniel Kessler (UNC Chapel Hill).
Speaker bio:
Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. She has received a number of awards for her research in statistical machine learning: most notably the Spiegelman Award from the American Public Health Association for a (bio)statistician under age 40, and the Presidents’ Award from the Committee of Presidents of Statistical Societies for a statistician under age 41. Daniela is a co-author of the textbook "Introduction to Statistical Learning".
May 1, 2025 - 2:35pm