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A Jackknifed estimators for the negative binomial regression model
Communications in Statistics - Simulation and Computation, 2017AbstractShrinkage estimator is a commonly applied solution to the general problem caused by multicollinearity. Recently, the ridge regression (RR) estimators for estimating the ridge parameter k in the negative binomial (NB) regression have been proposed. The Jackknifed estimators are obtained to remedy the multicollinearity and reduce the bias.
Semra Türkan, Gamze Özel
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2007
This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with
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This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with
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Geographically Weighted Negative Binomial Regression—incorporating overdispersion
Statistics and Computing, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alan Ricardo da Silva +1 more
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Semiparametric Negative Binomial Regression Models
Communications in Statistics - Simulation and Computation, 2010Negative-binomial (NB) regression models have been widely used for analysis of count data displaying substantial overdispersion (extra-Poisson variation). However, no formal lack-of-fit tests for a postulated parametric model for a covariate effect have been proposed. Therefore, a flexible parametric procedure is used to model the covariate effect as a
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Seemingly Unrelated Negative Binomial Regression
Oxford Bulletin of Economics and Statistics, 2000This paper discusses the specification and estimation of seemingly unrelated multivariate count data models. A new model with negative binomial marginals is proposed. In contrast to a previous model based on the multivariate Poisson distribution, the new model allows for over‐dispersion, a phenomenon that is frequently encountered in economic count ...
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Overdispersed negative binomial regression models
Communications in Statistics - Theory and Methods, 1992Overdispersion is a common phenomenon in actual data sets. It is important to have methods of dealing with extra variation in regression situations. This article develops tests for extra-negative binomial variation and gives some numerical methods to deal with it.
Dixi Xue, James A. Deddens
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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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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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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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