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....

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Detalles Bibliográficos
Autor principal: Fischer, William H. (Autor)
Otros Autores: Hartwell, Stephanie W. ; Deng, Xiaogang
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2017
En: Crime & delinquency
Año: 2017, Volumen: 63, Número: 1, Páginas: 77-87
Acceso en línea: Presumably Free Access
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Palabras clave:
Count regression models
Mental health
Offending
Psychische Gesundheit
Regressionsmodelle
Straftaten
Descripción
Sumario: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.
ISSN:1552-387X
DOI:10.1177/0011128716679796