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Parameter estimation on zero-inflated negative binomial regression with right truncated data
Seyed Ehsan Saffari1, Robiah Adnan2.
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 goodnessof-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.
Affiliation:
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
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MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
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0 |
Indexed by |
Web of Science (SCIE - Science Citation Index Expanded) |
Impact Factor
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JCR (1.009) |
Rank |
Q4 (Multidisciplinary Sciences) |
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JCI (0.15) |
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Scopus 2020 |
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CiteScore (1.4) |
Rank |
Q2 (Multidisciplinary) |
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SJR (0.251) |
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