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Inference on unknown parameters of a Burr distribution under hybrid censoring

Content Provider	Springer Nature Link
Author	Rastogi, Maj Kumar Tripathi, Yogesh Mani
Copyright Year	2012
Abstract	Based on hybrid censored data, the problem of making statistical inference on parameters of a two parameter Burr Type XII distribution is taken up. The maximum likelihood estimates are developed for the unknown parameters using the EM algorithm. Fisher information matrix is obtained by applying missing value principle and is further utilized for constructing the approximate confidence intervals. Some Bayes estimates and the corresponding highest posterior density intervals of the unknown parameters are also obtained. Lindley’s approximation method and a Markov Chain Monte Carlo (MCMC) technique have been applied to evaluate these Bayes estimates. Further, MCMC samples are utilized to construct the highest posterior density intervals as well. A numerical comparison is made between proposed estimates in terms of their mean square error values and comments are given. Finally, two data sets are analyzed using proposed methods.
Ending Page	643
Page Count	25
Starting Page	619
File Format	PDF
ISSN	09325026
e-ISSN	16139798
Journal	Statistical Papers
Issue Number	3
Volume Number	54
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-05-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Loss functions Estimation Lindley approximation method Probability Theory and Stochastic Processes Bayes estimates Hybrid type I censoring Statistics for Business/Economics/Mathematical Finance/Insurance Point estimation Operations Research/Decision Theory Economic Theory Maximum likelihood estimates Bayesian inference EM algorithm Importance sampling
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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