Seminar Details

Seminar Details


Monday

Jul 25

9:30 am

Improving Serfling's Inequality for the Hypergeometric Distribution

Evan Greene

Final Exam

Advisor: Jon Wellner

Abstract: We discuss a method for obtaining finite sample Gaussian bounds for the tail of the hypergeometric distribution. The method is based on Tusnády's approach (1975) to bounding the tail of symmetric binomial random variables. In this talk, we review Tusnády's result, and discuss how it can be adapted to and extended in the hypergeometric case. We discuss how these bounds, combined with bounds for the hypergeometric tail that incorporate information about the population variance into the bound, imply an improvement to Serfling's inequality (1974) when the hypergeometric parameters are constrained.