Parameter estimation on zero-inflated negative binomial regression with right truncated data
A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression m...
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| Main Authors: | , |
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| Format: | Article |
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Sains Malaysiana
2012
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| Online Access: | http://eprints.utm.my/id/eprint/32989/ http://www.ukm.my/jsm/pdf_files/SM-PDF-41-11-2012/19%20Seyed%20Ehsan.pdf |
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| Summary: | A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method was discussed and the goodness-of-fit for the regression model was examined. We studied the effects of truncation in terms of parameters estimation, their standard errors and the goodness- of-fit statistics via real data. The results showed a better fit by using a truncated zero-inflated negative binomial regression model when the response variable has many zeros and it was right truncated. |
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