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Author
Singh Sanjay Kumar (Banaras Hindu University, India), Singh Umesh (Banaras Hindu University, India), Kumar Manoj (Sharda University, Greater Noida)
Title
Bayesian Inference for Exponentiated Pareto Model with Application to Bladder Cancer Remission Time
Source
Statistics in Transition, 2014, vol. 15, nr 3, s. 403-426, aneks, rys., tab., bibliogr. 25 poz.
Keyword
Symulacja Monte Carlo, Wnioskowanie bayesowskie, Estymacja
Monte Carlo simulation, Bayesian inference, Estimation
Note
summ.
Abstract
Maximum likelihood and Bayes estimators of the unknown parameters and the expected experiment times of the exponentiated Pareto model have been obtained for progressive type-II censored data with binomial removal scheme. Markov Chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of the parameters of interest. The generalized entropy loss function and squared error loss function have been considered for obtaining the Bayes estimators. Comparisons are made between Bayesian and maximum likelihood (ML) estimators via Monte Carlo simulation. The proposed methodology is illustrated through real data. (original abstract)
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The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of the Wroclaw University of Economics
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Bibliography
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ISSN
1234-7655
Language
eng
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