Regression Models for Multivariate Count Data
نویسندگان
چکیده
منابع مشابه
Latent feature regression for multivariate count data
We consider the problem of regression on multivariate count data and present a Gibbs sampler for a latent feature regression model suitable for both underand overdispersed response variables. The model learns countvalued latent features conditional on arbitrary covariates, modeling them as negative binomial variables, and maps them into the dependent count-valued observations using a Dirichlet-...
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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 well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
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We consider panel count data which are frequently obtained in prospective studies involving recurrent events that are only detected and recorded at periodic assessment times. The data take the form of counts of the cumulative number of events detected at each inspection time, along with explanatory covariates. Examples arise in diverse areas such as epidemiological studies, medical follow-up st...
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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions ...
متن کاملGeneralized bivariate count data regression models
This paper proposes a flexible bivariate count data regression model that nests the bivariate negative binomial regression. An application to the demand for health services is given. 2000 Elsevier Science S.A. All rights reserved.
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2017
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2016.1154063