title: Influence diagnostics for two-component Poisson mixture regression models: applications in public health.
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.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Australia.
authors.forename: Liming
authors.initials: L
authors.lastname: Xiang
authors.forename: Kelvin K W
authors.initials: KK
authors.lastname: Yau
authors.forename: Andy H
authors.initials: AH
authors.lastname: Lee
authors.forename: Wing K
authors.initials: WK
authors.lastname: Fung
mesh_terms: D016000:Cluster Analysis; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D036861:Delivery, Obstetric; D005260:Female; D006296:Health Services; D006801:Humans; D007902:Length of Stay; D016012:Poisson Distribution; D011247:Pregnancy; D011634:Public Health; D012044:Regression Analysis; D012189:Retrospective Studies; D014552:Urinary Tract Infections / Q000453:epidemiology; D014904:Western Australia / Q000453:epidemiology