Managing inflation: on the use and potential misuse of zero-inflated count regression models
Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data....
| Authors: | ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2017
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| In: |
Crime & delinquency
Year: 2017, Volume: 63, Issue: 1, Pages: 77-87 |
| Online Access: |
Presumably Free Access Volltext (Resolving-System) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
MARC
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| 100 | 1 | |a Fischer, William H. |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Managing inflation |b on the use and potential misuse of zero-inflated count regression models |c William H. Fisher, Stephanie W. Hartwell, and Xiaogang Deng |
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| 520 | |a Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction. | ||
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