[Seminar] Selection bias may be adjusted when sample size is negative " by Prof. Hidetoshi Shimodaira

Abstract:

For computing p-values, you should specify hypotheses before looking at data. However, people tend to use dataset twice for hypothesis selection and evaluation, leading to inflated statistical significance and more false positives than expected. Recently, a new statistical method, called selective inference or post selection inference, has been developed for adjusting this selection bias. In this talk, I present a bootstrap resampling method with “negative sample size” for computing bias corrected p-values. Examples are shown for confidence interval of regression coefficients after model selection, and significance levels of trees and edges in hierarchical clustering and phylogenetic inference.

Biography:

Hidetoshi Shimodaira is a professor at Kyoto University and a team leader at RIKEN AIP. He has been working on theory and methods of statistics and machine learning. His multiscale bootstrap method is used in genomics for evaluating statistical significance of trees and clusters. His “covariate shift” setting for transfer learning is popular in machine learning.