A game-theoretic theory of statistical evidence

Date: 
February 19, 2025
Time: 
3 to 4 p.m. PT
Place: 
Zoom seminar

Aaditya Ramdas, PhD
Associate Professor of Statistics and Data Science, Carnegie Mellon University

This talk will describe an approach towards testing hypotheses and estimating functionals that is based on games. In short, to test a (possibly composite, nonparametric) hypothesis, we set up a game in which no betting strategy can make money under the null (the wealth is an “e-process” under the null). But if the null is false, then smart betting strategies will have exponentially increasing wealth. Thus, hypotheses are rewritten as constraints in games, the statistician is a gambler, test statistics are betting strategies, and the wealth obtained is directly a measure of evidence which is valid at any data-dependent stopping time (an e-value). The optimal betting strategies are typically Bayesian, but the guarantees are frequentist. This “game perspective” provides new statistically and computationally efficient solutions to many modern problems.

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Event Type: 
Biostatistics and Bioinformatics Seminar