Categorical Data Analysis Using a Skewed Weibull Regression Model

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 fu...

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Detalles Bibliográficos
Autores principales: Caron, Renault, Sinha, Debajyoti, Dey, Dipak K., Polpo, Adriano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512693/
https://www.ncbi.nlm.nih.gov/pubmed/33265267
http://dx.doi.org/10.3390/e20030176
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author Caron, Renault
Sinha, Debajyoti
Dey, Dipak K.
Polpo, Adriano
author_facet Caron, Renault
Sinha, Debajyoti
Dey, Dipak K.
Polpo, Adriano
author_sort Caron, Renault
collection PubMed
description 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.
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spelling pubmed-75126932020-11-09 Categorical Data Analysis Using a Skewed Weibull Regression Model Caron, Renault Sinha, Debajyoti Dey, Dipak K. Polpo, Adriano Entropy (Basel) Article 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. MDPI 2018-03-07 /pmc/articles/PMC7512693/ /pubmed/33265267 http://dx.doi.org/10.3390/e20030176 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Caron, Renault
Sinha, Debajyoti
Dey, Dipak K.
Polpo, Adriano
Categorical Data Analysis Using a Skewed Weibull Regression Model
title Categorical Data Analysis Using a Skewed Weibull Regression Model
title_full Categorical Data Analysis Using a Skewed Weibull Regression Model
title_fullStr Categorical Data Analysis Using a Skewed Weibull Regression Model
title_full_unstemmed Categorical Data Analysis Using a Skewed Weibull Regression Model
title_short Categorical Data Analysis Using a Skewed Weibull Regression Model
title_sort categorical data analysis using a skewed weibull regression model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512693/
https://www.ncbi.nlm.nih.gov/pubmed/33265267
http://dx.doi.org/10.3390/e20030176
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AT sinhadebajyoti categoricaldataanalysisusingaskewedweibullregressionmodel
AT deydipakk categoricaldataanalysisusingaskewedweibullregressionmodel
AT polpoadriano categoricaldataanalysisusingaskewedweibullregressionmodel

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