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The VGAM package for negative binomial regression
Australian & New Zealand Journal of Statistics, 2020Negative binomial (NB) regression is the most common full‐likelihood method for analysing count data exhibiting overdispersion with respect to the Poisson distribution. Usually most practitioners are content to fit one of two NB variants, however other important variants exist.
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Negative binomial and mixed Poisson regression
Canadian Journal of Statistics, 1987AbstractA number of methods have been proposed for dealing with extra‐Poisson variation when doing regression analysis of count data. This paper studies negative‐binomial regression models and examines efficiency and robustness properties of inference procedures based on them. The methods are compared with quasilikelihood methods.
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A new bivariate negative binomial regression model
AIP Conference Proceedings, 2014This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f.
Pouya Faroughi, Noriszura Ismail
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On the bivariate negative binomial regression model
Journal of Applied Statistics, 2010In this paper, a new bivariate negative binomial regression (BNBR) model allowing any type of correlation is defined and studied. The marginal means of the bivariate model are functions of the explanatory variables. The parameters of the bivariate regression model are estimated by using the maximum likelihood method.
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Improving Estimation of Regression Parameters in Negative Binomial Regression Model
2018In this study, we proposed a preliminary test strategy that incorporated subspace information into parameter estimation using a negative binomial regression model. The relative performance of proposed estimators was investigated through both Monte Carlo simulations and application to a real dataset, using the classical maximum likelihood estimator as ...
Orawan Reangsephet +2 more
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Negative binomial regression with application in autorating
1998Thesis - Athens University of Economics and Business.
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Testing the linearity of negative binomial regression models
Journal of Statistical Computation and Simulation, 2013The negative binomial (NB) is frequently used to model overdispersed Poisson count data. To study the effect of a continuous covariate of interest in an NB model, a flexible procedure is used to model the covariate effect by fixed-knot cubic basis-splines or B-splines with a second-order difference penalty on the adjacent B-spline coefficients to avoid
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Modelling the Claim Count with Poisson Regression and Negative Binomial Regression
2005It is of interest for an insurance company to model the claim count or the claim severity in the presence of covariates or covariate factors describing the policyholders. In this paper the Poisson regression is presented (in the context of generalized linear models) and fitted to car insurance claims data. Since there are symptoms of over-dispersion in
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