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Activity Number: 81 - Regression, Distribution and Inference
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: International Indian Statistical Association
Abstract #302910 Presentation
Title: Uniformly Consistently Estimating the Proportion of False Null Hypotheses via Lebesgue-Stieltjes Integral Equations
Author(s): Xiongzhi Chen*
Companies: Washington State University
Keywords: Lebesgue-Stieltjes integral equation; multiple hypotheses testing; natural exponential family; proportion of false null hypotheses
Abstract:

The proportion of false null hypotheses is a very important quantity in statistical modelling and inference based on the two-component mixture model and its extensions, and in control and estimation of the false discovery rate and false non-discovery rate. Most existing estimators of this proportion threshold p-values, deconvolve the mixture model under constraints on its components, or depend heavily on the location-shift property of distributions. Hence, they usually are not consistent, applicable to non-location-shift distributions, or applicable to discrete statistics or p-values. To eliminate these shortcomings, we construct uniformly consistent estimators of the proportion as solutions to Lebesgue-Stieltjes integral equations. In particular, we provide such estimators respectively for random variables whose distributions belong to three types of natural exponential families, and the speed of convergence and uniform consistency class for each such estimator under independence. In addition, we provide example distribution families for which a consistent estimator of the proportion cannot be constructed using our techniques.


Authors who are presenting talks have a * after their name.

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