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 mod...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2012
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| Online Access: | http://journalarticle.ukm.my/5586/ http://journalarticle.ukm.my/5586/ http://journalarticle.ukm.my/5586/1/19%2520Seyed%2520Ehsan.pdf |
| 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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