Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was...
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doaj-e068c1032f85476da66ab5b85320ab4e2020-11-24T22:40:13ZengMDPI AGJournal of Risk and Financial Management1911-80742018-03-011111310.3390/jrfm11010013jrfm11010013Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian AnalysisCarlos A. dos Santos0Daniele C. T. Granzotto1Vera L. D. Tomazella2Francisco Louzada3Department of Statistics, State University of Maringá, 87020-900 Maringá-PR, BrazilDepartment of Statistics, State University of Maringá, 87020-900 Maringá-PR, BrazilDepartment of Statistics, Federal University of São Carlos, 13565-905 São Carlos-SP, BrazilMath Science Institute and Computing, University of São Paulo, 13560-970 São Carlos-SP, BrazilIn this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was formulated by using the quadratic transmutation map, that is a simple way of derivating new distributions, and it adds a new parameter λ , which one introduces a skewness in the new distribution and preserves the moments of the baseline model. The Bayesian model was formulated by using the half-Cauchy prior which is an alternative prior to a inverse Gamma distribution. In order to fit the model, a real data set, which consist of the time up to first calving of polled Tabapua race, was used. Finally, after the model was fitted, an influential analysis was made and excluding only 0.1 % of observations (influential points), the reestimated model can fit the data better.http://www.mdpi.com/1911-8074/11/1/13hierarchical Bayesian modelinfluential analysislog-logistic distributiontransmuted map |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carlos A. dos Santos Daniele C. T. Granzotto Vera L. D. Tomazella Francisco Louzada |
spellingShingle |
Carlos A. dos Santos Daniele C. T. Granzotto Vera L. D. Tomazella Francisco Louzada Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis Journal of Risk and Financial Management hierarchical Bayesian model influential analysis log-logistic distribution transmuted map |
author_facet |
Carlos A. dos Santos Daniele C. T. Granzotto Vera L. D. Tomazella Francisco Louzada |
author_sort |
Carlos A. dos Santos |
title |
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis |
title_short |
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis |
title_full |
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis |
title_fullStr |
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis |
title_full_unstemmed |
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis |
title_sort |
hierarchical transmuted log-logistic model: a subjective bayesian analysis |
publisher |
MDPI AG |
series |
Journal of Risk and Financial Management |
issn |
1911-8074 |
publishDate |
2018-03-01 |
description |
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was formulated by using the quadratic transmutation map, that is a simple way of derivating new distributions, and it adds a new parameter λ , which one introduces a skewness in the new distribution and preserves the moments of the baseline model. The Bayesian model was formulated by using the half-Cauchy prior which is an alternative prior to a inverse Gamma distribution. In order to fit the model, a real data set, which consist of the time up to first calving of polled Tabapua race, was used. Finally, after the model was fitted, an influential analysis was made and excluding only 0.1 % of observations (influential points), the reestimated model can fit the data better. |
topic |
hierarchical Bayesian model influential analysis log-logistic distribution transmuted map |
url |
http://www.mdpi.com/1911-8074/11/1/13 |
work_keys_str_mv |
AT carlosadossantos hierarchicaltransmutedloglogisticmodelasubjectivebayesiananalysis AT danielectgranzotto hierarchicaltransmutedloglogisticmodelasubjectivebayesiananalysis AT veraldtomazella hierarchicaltransmutedloglogisticmodelasubjectivebayesiananalysis AT franciscolouzada hierarchicaltransmutedloglogisticmodelasubjectivebayesiananalysis |
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1725705358454816768 |