Hurdle negative binomial regression model with right censored count data

Hurdle negative binomial regression model with right censored count data
Ehsan Saffari, Seyed (Universiti Teknologi Malaysia. Department of Mathematics, Faculty of Science)
Adnan, Robiah (Universiti Teknologi Malaysia. Department of Mathematics, Faculty of Science)
Greene, William (New York University. Department of Economics, Stern School of Business)

Data:	2012
Resum:	A Poisson model typically is assumed for count data. In many cases because of many zeros in the response variable, the mean is not equal to the variance value of the dependent variable. Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest using a hurdle negative binomial regression model to overcome the problem of overdispersion. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle negative binomial regression model is introduced on count data with many zeros. The estimation of regression parameters using maximum likelihood is discussed and the goodness-of-fit for the regression model is examined.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Hurdle negative binomial regression ; Censored data ; Maximum likelihood method ; Simulation
Publicat a:	SORT : statistics and operations research transactions, juliol-desembre 2012, p. 181-194, ISSN 2013-8830

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Registre creat el 2013-01-09, darrera modificació el 2022-02-13