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A score‐type test for heterogeneity in zero‐inflated models in a stratified population
Author(s) -
Cao Guanqun,
Hsu WeiWen,
Todem David
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6092
Subject(s) - statistics , null hypothesis , statistic , econometrics , type i and type ii errors , test statistic , null distribution , mathematics , population , statistical hypothesis testing , homogeneity (statistics) , parametric statistics , stratification (seeds) , computer science , medicine , seed dormancy , botany , germination , environmental health , dormancy , biology
We propose a score‐type statistic to evaluate heterogeneity in zero‐inflated models for count data in a stratified population, where heterogeneity is defined as instances in which the zero counts are generated from two sources. Evaluating heterogeneity in this class of models has attracted considerable attention in the literature, but existing testing procedures have primarily relied on the constancy assumption under the alternative hypothesis. In this paper, we extend the literature by describing a score‐type test to evaluate homogeneity against general alternatives that do not neglect the stratification information under the alternative hypothesis. The limiting null distribution of the proposed test statistic is a mixture of chi‐squared distributions that can be well approximated by a simple parametric bootstrap procedure. Our numerical simulation studies show that the proposed test can greatly improve efficiency over tests of heterogeneity that ignore the stratification information. An empirical application to dental caries data in early childhood further shows the importance and practical utility of the methodology in using the stratification profile to detect heterogeneity in the population. Copyright © 2014 John Wiley & Sons, Ltd.