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estimation of count data using bivariate negative binomial regression models
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نویسنده
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faroughi pouya ,karimi mohaamad sharif ,noriszura ismail ,karimi asrin
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منبع
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اقتصاد مقداري (بررسي هاي اقتصادي سابق) - 1396 - دوره : 14 - شماره : 2 - صفحه:143 -166
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چکیده
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Negative binomial regression model (nbr) is a popular approach for modeling overdispersed count data with covariates. several parameterizations have been performed for nbr, and the two wellknown models, negative binomial1 regression model (nbr1) and negative binomial2 regression model (nbr2), have been applied. another parameterization of nbr is negative binomialp regression model (nbrp), which has an additional parameter and the ability to nest both nbr1 and nbr2. this paper introduces several forms of bivariate negative binomial regression model (bnbr) which can be fitted to bivariate count data with covariates. the main advantages of having several forms of bnbr are that they are nested and allow likelihood ratio test to be performed for choosing the best model, they have flexible forms of meanvariance relationship, they can be fitted to bivariate count data with positive, zero or negative correlations, and they allow overdispersion of the two dependent variables. applications of several forms of bnbr are illustrated on two sets of count data; australian health care and malaysian motor insurance.
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کلیدواژه
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bivariate poisson regression model ,bivariate negative binomial regression model ,correlation ,over dispersion
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آدرس
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islamic azad university, sanandaj branch, department of statistics, ایران, razi university, faculty of social sciences, department of economics, ایران, universiti kebangsaan malaysia, faculty of science and tchnology, school of mathematical sciences, malaysia, university putra malaysia, faculty of economics and management, malaysia
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پست الکترونیکی
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asrin.karimi@gmail.com
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Estimation of Count Data using Bivariate Negative Binomial Regression Models
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Authors
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Faroughi Pouya ,Karimi Mohaamad Sharif ,Noriszura Ismail ,Karimi Asrin
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Abstract
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Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two wellknown models, negative binomial1 regression model (NBR1) and negative binomial2 regression model (NBR2), have been applied. Another parameterization of NBR is negative binomialP regression model (NBRP), which has an additional parameter and the ability to nest both NBR1 and NBR2. This paper introduces several forms of bivariate negative binomial regression model (BNBR) which can be fitted to bivariate count data with covariates. The main advantages of having several forms of BNBR are that they are nested and allow likelihood ratio test to be performed for choosing the best model, they have flexible forms of meanvariance relationship, they can be fitted to bivariate count data with positive, zero or negative correlations, and they allow overdispersion of the two dependent variables. Applications of several forms of BNBR are illustrated on two sets of count data; Australian health care and Malaysian motor insurance.
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Keywords
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bivariate Poisson regression model ,bivariate negative binomial regression model ,Correlation ,overdispersion
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