Influence diagnostics for two-component Poisson mixture regression models: applications in public health
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
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John Wiley and Sons
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/25537 |
| _version_ | 1848751737044729856 |
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| author | Xiang, Liming Lee, Andy Yau, K. Fung, W. |
| author_facet | Xiang, Liming Lee, Andy Yau, K. Fung, W. |
| author_sort | Xiang, Liming |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:57:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-25537 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:57:28Z |
| publishDate | 2005 |
| publisher | John Wiley and Sons |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-255372017-09-13T15:51:05Z Influence diagnostics for two-component Poisson mixture regression models: applications in public health Xiang, Liming Lee, Andy Yau, K. Fung, W. count data Poisson mixture regression random effects local influence heterogeneity diagnostics 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. 2005 Journal Article http://hdl.handle.net/20.500.11937/25537 10.1002/sim.2160 John Wiley and Sons restricted |
| spellingShingle | count data Poisson mixture regression random effects local influence heterogeneity diagnostics Xiang, Liming Lee, Andy Yau, K. Fung, W. Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title | Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title_full | Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title_fullStr | Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title_full_unstemmed | Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title_short | Influence diagnostics for two-component Poisson mixture regression models: applications in public health |
| title_sort | influence diagnostics for two-component poisson mixture regression models: applications in public health |
| topic | count data Poisson mixture regression random effects local influence heterogeneity diagnostics |
| url | http://hdl.handle.net/20.500.11937/25537 |