Count Regression Models with an Application to Zoological Data Containing Structural Zeros

Hurdle, Inflated Poisson and Inflated Negative Binomial Regression Models ‎ for Analysis of Count Data with Extra Zeros

In this paper‎, ‎we ‎propose ‎Hurdle regression models for analysing count responses with extra zeros‎. A method of estimating maximum likelihood is used to estimate model parameters. The application of the proposed model is presented in insurance dataset‎. In this example‎, there are many numbers of claims equal to zero is considered that clarify the application of the model with a zero-inflat...

Models for count data with many zeros

Poisson regression models provide a standard framework for the analysis of count data. In practice, however, count data are often overdispersed relative to the Poisson distribution. One frequent manifestation of overdispersion is that the incidence of zero counts is greater than expected for the Poisson distribution and this is of interest because zero counts frequently have special status. For...

Estimation of Count Data using Bivariate Negative Binomial Regression Models

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...

social hidden groups size analyzing: application of count regression models for excess zeros

background: in the case of sensitive questions such as number of alcoholics known, majority of respondents might give an answer of zero. poisson regression model (p) is the standard tool to analyze count data. however, p provides poor fit in the case of zero inflated counts, when over-dispersion exists. therefore, the questions to be addressed are to compare performance of alternative count reg...

Zero-inflated regression models for count data: an application to under-5 deaths