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...
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Universiti Kebangsaan Malaysia
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ukm-55862016-12-14T06:38:53Z http://journalarticle.ukm.my/5586/ Parameter estimation on zero-inflated negative binomial regression with right truncated data Seyed Ehsan Saffari, Robiah Adnan, 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. Universiti Kebangsaan Malaysia 2012-11 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/5586/1/19%2520Seyed%2520Ehsan.pdf Seyed Ehsan Saffari, and Robiah Adnan, (2012) Parameter estimation on zero-inflated negative binomial regression with right truncated data. Sains Malaysiana, 41 (11). pp. 1483-1487. ISSN 0126-6039 http://www.ukm.my/jsm/ |
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Universiti Kebangasaan Malaysia |
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Online Access |
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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. |
format |
Article |
author |
Seyed Ehsan Saffari, Robiah Adnan, |
spellingShingle |
Seyed Ehsan Saffari, Robiah Adnan, Parameter estimation on zero-inflated negative binomial regression with right truncated data |
author_facet |
Seyed Ehsan Saffari, Robiah Adnan, |
author_sort |
Seyed Ehsan Saffari, |
title |
Parameter estimation on zero-inflated negative binomial regression with right truncated data |
title_short |
Parameter estimation on zero-inflated negative binomial regression with right truncated data |
title_full |
Parameter estimation on zero-inflated negative binomial regression with right truncated data |
title_fullStr |
Parameter estimation on zero-inflated negative binomial regression with right truncated data |
title_full_unstemmed |
Parameter estimation on zero-inflated negative binomial regression with right truncated data |
title_sort |
parameter estimation on zero-inflated negative binomial regression with right truncated data |
publisher |
Universiti Kebangsaan Malaysia |
publishDate |
2012 |
url |
http://journalarticle.ukm.my/5586/ http://journalarticle.ukm.my/5586/ http://journalarticle.ukm.my/5586/1/19%2520Seyed%2520Ehsan.pdf |
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2023-09-18T19:44:34Z |
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2023-09-18T19:44:34Z |
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