Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis
Department of Statistics, State University of Maringá, 87020-900 Maringá-PR, Brazil
Department of Statistics, Federal University of São Carlos, 13565-905 São Carlos-SP, Brazil
Math Science Institute and Computing, University of São Paulo, 13560-970 São Carlos-SP, Brazil
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 30 December 2017 / Revised: 2 March 2018 / Accepted: 5 March 2018 / Published: 7 March 2018
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
, 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
of observations (influential points), the reestimated model can fit the data better.
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dos Santos, C.A.; Granzotto, D.C.T.; Tomazella, V.L.D.; Louzada, F. Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis. J. Risk Financial Manag. 2018, 11, 13.
dos Santos CA, Granzotto DCT, Tomazella VLD, Louzada F. Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis. Journal of Risk and Financial Management. 2018; 11(1):13.
dos Santos, Carlos A.; Granzotto, Daniele C.T.; Tomazella, Vera L.D.; Louzada, Francisco. 2018. "Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis." J. Risk Financial Manag. 11, no. 1: 13.
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