Influence diagnostics for two-component Poisson mixture regression models : Applications in public health

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)3053-3071
Journal / PublicationStatistics in Medicine
Volume24
Issue number19
Publication statusPublished - 15 Oct 2005

Abstract

In many medical and health applications, Poisson mixture regression models are commonly used to analyse heterogeneous count data. Motivated by two data sets drawn from public health studies, influence diagnostics are proposed for assessing the sensitivity of the fitted two-component Poisson mixture regression models. Under various perturbations of the observed data or model assumptions, influence assessments based on the local influence approach are developed for detecting clusters and/or individual observations that impact on the estimation of model parameters. Results from studies on recurrent urinary tract infections and maternity length of stay illustrate the usefulness of the influence diagnostics. Copyright © 2005 John Wiley & Sons, Ltd.

Research Area(s)

  • Count data, Diagnostics, Heterogeneity, Local influence, Poisson mixture regression, Random effects