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Activity Number: 193
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #317343
Title: Adjusted Supremum Score-Type Statistics for Evaluating Nonstandard Hypotheses
Author(s): Wei-Wen Hsu* and David Todem and KyungMann Kim
Companies: Kansas State University and Michigan State University and University of Wisconsin - Madison
Keywords: bias adjustment ; empirical processes ; non-identi?able parameters ; tests of homogeneity ; zero-in?ated models

Supremum score test statistics are often used to evaluate hypotheses with unidentifiable nuisance parameters under the null hypothesis. Although these statistics provide an attractive framework to address non-identifiability under the null hypothesis, little attention has been paid to their distributional properties in small to moderate sample size settings. In situations where there are identifiable nuisance parameters under the null hypothesis, these statistics may behave erratically in realistic samples as a result of a non-negligible bias induced by substituting these nuisance parameters by their estimates under the null hypothesis. In this paper, we propose an adjustment to the supremum score statistics by subtracting the expected bias from the score processes and show that this adjustment does not alter the limiting null distribution of the supremum score statistics. Using a simple example from the class of zero-inflated regression models for count data, we show empirically and theoretically that the adjusted tests are superior in terms of size and power. The practical utility of this methodology is illustrated using count data in HIV research.

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

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