Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model
A Poisson model typically is assumed for count data. It is assumed to have the same value for expectation and variance in a Poisson distribution, but most of the time there is over-dispersion in the model. Furthermore, the response variable in such cases is truncated for some outliers or large v...
| Main Authors: | , , |
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| Format: | Journal Article |
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Sandkrs Sdn Bhd
2011
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| Online Access: | http://www.jcscm.net/fmgr/download.php?id=1705649 http://hdl.handle.net/20.500.11937/18413 |
| _version_ | 1848749737498378240 |
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| author | Saffari, S.E. Adnan, R. Greene, William |
| author_facet | Saffari, S.E. Adnan, R. Greene, William |
| author_sort | Saffari, S.E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A Poisson model typically is assumed for count data. It is assumed to have the same value for expectation and variance in a Poisson distribution, but most of the time there is over-dispersion in the model. Furthermore, the response variable in such cases is truncated for some outliers or large values. In this paper, a Poisson regression model is introduced on truncated data. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness-of-fit for the regression model is examined. We study the effects of truncation in terms of parameters estimation and their standard errors via real data. |
| first_indexed | 2025-11-14T07:25:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-18413 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:25:42Z |
| publishDate | 2011 |
| publisher | Sandkrs Sdn Bhd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-184132017-01-30T12:07:43Z Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model Saffari, S.E. Adnan, R. Greene, William truncation - parameter estimation over-dispersion Poisson regression A Poisson model typically is assumed for count data. It is assumed to have the same value for expectation and variance in a Poisson distribution, but most of the time there is over-dispersion in the model. Furthermore, the response variable in such cases is truncated for some outliers or large values. In this paper, a Poisson regression model is introduced on truncated data. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness-of-fit for the regression model is examined. We study the effects of truncation in terms of parameters estimation and their standard errors via real data. 2011 Journal Article http://hdl.handle.net/20.500.11937/18413 http://www.jcscm.net/fmgr/download.php?id=1705649 Sandkrs Sdn Bhd restricted |
| spellingShingle | truncation - parameter estimation over-dispersion Poisson regression Saffari, S.E. Adnan, R. Greene, William Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title | Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title_full | Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title_fullStr | Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title_full_unstemmed | Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title_short | Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model |
| title_sort | handling of over-dispersion of count data via truncation using poisson regression model |
| topic | truncation - parameter estimation over-dispersion Poisson regression |
| url | http://www.jcscm.net/fmgr/download.php?id=1705649 http://hdl.handle.net/20.500.11937/18413 |