Categorical Data Analysis Using a Skewed Weibull Regression Model

2017/11/16 by Renault Caron, Debajyoti Sinha, Dipak K. Dey +2
Mathematics · #Advanced Statistical Methods and Models #Bayesian inference #Bayesian probability #Binomial (polynomial) #Categorical variable #Computer science #Econometrics #Frequentist inference #Mathematics #Multinomial distribution #Multinomial logistic regression #Multinomial probit #Probit model #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #Statistics #Weibull distribution #stat.ME

paper · pdf · doi:10.3390/e20030176

arxiv created 2017/11/16 · openalex publication_date 2018/03/07 · arxiv updated 2018/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in detail. The analysis of two datasets to show the efficiency of the proposed model is performed.

Citations

• Bayesian Measures of Model Complexity and Fit
• VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
• A Generalization of the Probit and Logit Methods for Dose Response Curves
• Bayesian Analysis of Binary and Polychotomous Response Data
• A new look at the statistical model identification
• A Simplex Method for Function Minimization
• Generalized Linear Models
• Bayes Factors