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

Xiang L, Yau KK, Lee AH, Fung WK

Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Australia.

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.

Published 21 September 2005 in Stat Med, 24(19): 3053-71.
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