Bayesian Estimation for the Exponentiated Weibull Model via Markov Chain Monte Carlo Simulation

被引：21

作者：

Jaheen, Zeinhum F. ^{[1
]}

Al Harbi, Mashail M. ^{[2
]}

机构：

[1] King Abdulaziz Univ, Dept Stat, Jeddah 21589, Saudi Arabia

[2] Umm Al Qura Univ, Dept Math, Appl Sci Coll, Mecca, Saudi Arabia

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2011年 / 40卷 / 04期

关键词：

Balanced loss function; Bayes estimation; Exponentiated-Weibull model; Generalized order statistics; Markov chain Monte Carlo (MCMC); GENERAL-CLASS; DISTRIBUTIONS; FAMILY; PARAMETERS;

D O I：

10.1080/03610918.2010.546543

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Bayesian estimation for the two unknown parameters and the reliability function of the exponentiated Weibull model are obtained based on generalized order statistics. Markov chain Monte Carlo (MCMC) methods are considered to compute the Bayes estimates of the target parameters. Our computations are based on the balanced loss function which contains the symmetric and asymmetric loss functions as special cases. The results have been specialized to the progressively Type-II censored data and upper record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

引用

页码：532 / 543

页数：12

共 50 条

[41] Markov chain Monte Carlo estimation for the two-component model
Jones, G
[J]. TECHNOMETRICS, 2004, 46 (01) : 99 - 107
[42] Parameter estimation by a Markov chain Monte Carlo technique for the Candy model
Descombes, X
van Lieshout, MNM
Stoica, R
Zerubia, J
[J]. 2001 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING PROCEEDINGS, 2001, : 22 - 25
[43] Parameter Estimation in Population Balance through Bayesian Technique Markov Chain Monte Carlo
Moura, Carlos H. R.
Viegas, Bruno M.
Tavares, Maria R. M.
Macedo, Emanuel N.
Estumano, Diego C.
Quaresma, Joao N. N.
[J]. JOURNAL OF APPLIED AND COMPUTATIONAL MECHANICS, 2021, 7 (02): : 890 - 901
[44] Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling
Vrugt, Jasper A.
Diks, Cees G. H.
Clark, Martyn P.
[J]. ENVIRONMENTAL FLUID MECHANICS, 2008, 8 (5-6) : 579 - 595
[45] Bayesian analysis of the discovery process model using Markov chain Monte Carlo
Sinding-Larsen R.
Xu J.
[J]. Natural Resources Research, 2005, 14 (4) : 333 - 344
[46] BAYESIAN CALIBRATION OF A CERVICAL CANCER MODEL USING MARKOV CHAIN MONTE CARLO
Walsh, C.
Ortendahl, J.
Sy, S.
Kim, J.
[J]. VALUE IN HEALTH, 2011, 14 (07) : A470 - A470
[47] A Differential Evolution Markov Chain Monte Carlo Algorithm for Bayesian Model Updating
Sherri, M.
Boulkaibet, I
Marwala, T.
Friswell, M., I
[J]. SPECIAL TOPICS IN STRUCTURAL DYNAMICS, VOL 5, 2019, : 115 - 125
[48] Bayesian model selection for time series using Markov chain Monte Carlo
Troughton, PT
Godsill, SJ
[J]. 1997 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I - V: VOL I: PLENARY, EXPERT SUMMARIES, SPECIAL, AUDIO, UNDERWATER ACOUSTICS, VLSI; VOL II: SPEECH PROCESSING; VOL III: SPEECH PROCESSING, DIGITAL SIGNAL PROCESSING; VOL IV: MULTIDIMENSIONAL SIGNAL PROCESSING, NEURAL NETWORKS - VOL V: STATISTICAL SIGNAL AND ARRAY PROCESSING, APPLICATIONS, 1997, : 3733 - 3736
[49] Markov Chain Monte Carlo Estimation for Bayesian Approach Based on Right Censored Data
Ahmed, Alomari Mohammed
[J]. PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES, 2014, 1602 : 1009 - 1015
[50] Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods
Andrieu, C
Doucet, A
Duvaut, P
[J]. THIRTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2, 1998, : 1097 - 1101

← 1 2 3 4 5 →