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Influence diagnostics for two‐component Poisson mixture regression models: applications in public health
Author(s) -
Xiang Liming,
Yau Kelvin K. W.,
Lee Andy H.,
Fung Wing K.
Publication year - 2005
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.2160
Subject(s) - poisson regression , poisson distribution , statistics , count data , component (thermodynamics) , regression , econometrics , regression analysis , computer science , sensitivity (control systems) , data mining , mathematics , medicine , environmental health , population , engineering , physics , electronic engineering , thermodynamics
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.