Quantile Regression for Count Data as a Robust Alternative to Negative Binomial Regression

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...

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...

Quantile regression for overdispersed count data: a hierarchical method

Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed c...

Disease Mapping via Negative Binomial M-quantile Regression

Analysis of U.S. freight-train derailment severity using zero-truncated negative binomial regression and quantile regression.

Derailments are the most common type of freight-train accidents in the United States. Derailments cause damage to infrastructure and rolling stock, disrupt services, and may cause casualties and harm the environment. Accordingly, derailment analysis and prevention has long been a high priority in the rail industry and government. Despite the low probability of a train derailment, the potential ...