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Activity Number: 601 - Recent Advances in Variable Selection for Linear and Nonlinear Models
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304821 Presentation
Title: Nonuniformity of P-Values Can Occur Early in Diverging Dimensions
Author(s): Emre Demirkaya*
Companies: University of Southern California
Keywords: p-value; nonuniformity; high dimensionality; generalized linear model; joint significance testing; breakdown point

Evaluating the joint significance of covariates is of fundamental importance in a wide range of applications. To this end, p-values are frequently employed and produced by algorithms that are powered by classical large-sample asymptotic theory. It is well known that the conventional p-values in Gaussian linear model are valid even when the dimensionality is a non-vanishing fraction of the sample size, but can break down when the design matrix becomes singular in higher dimensions or when the error distribution deviates from Gaussianity. A natural question is when the conventional p-values in generalized linear models become invalid in diverging dimensions. We establish that such a breakdown can occur early in nonlinear models. Our theoretical characterizations are confirmed by simulation studies.

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

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