Bayesian estimation of parameters of mixed geometric failure models from Type I group censored sample

2009 ◽  
Vol 36 (5) ◽  
pp. 495-506
Author(s):  
J. B. Shah ◽  
M. N. Patel
Keyword(s):  
Bayesian Estimation ◽  
Type I ◽  
Estimation Of Parameters ◽  
Censored Sample ◽  
Failure Models

scholarly journals Estimations of the parameters of generalised exponential distribution under progressive interval type-I censoring scheme with random removals

2017 ◽  
Vol 46 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Arun Kaushik ◽  
Aakriti Pandey ◽  
Sandeep Kumar Maurya ◽  
Umesh Singh ◽  
Sanjay Kumar Singh
Keyword(s):  
Exponential Distribution ◽  
Interval Estimation ◽  
Type I ◽  
Estimation Of Parameters ◽  
Type I Censoring ◽  
Bayesian Procedures ◽  
Maximum Likelihood Expectation Maximization ◽  
Censored Sample ◽  
Interval Type ◽  
Censoring Scheme

The present article aims to point and interval estimation of the parameters of generalised exponential distribution (GED) under progressive interval type-I (PITI) censoring scheme with random removals. The considered censoring scheme is most useful in those cases where continuous examination is not possible. Maximum likelihood, expectation-maximization and Bayesian procedures have been developed for the estimation of parameters of the GED, based on a PITI censored sample. Real datasets have been considered to illustrate the applicability of the proposed work. Further, we have compared the performances of the proposed estimators under PITI censoring to that of the complete sample.

scholarly journals Bayesian estimation of the parameters of the bivariate generalized exponential distribution using accelerated life testing under censoring data

2015 ◽  
Vol 3 (2) ◽  
pp. 215
Author(s):  
Abd El-Maseh, M. P
Keyword(s):  
Bayesian Estimation ◽  
Accelerated Life Testing ◽  
Constant Stress ◽  
Type I ◽  
Life Testing ◽  
Unknown Parameters ◽  
Numerical Studies ◽  
Inverse Power Law ◽  
Accelerated Life ◽  
Power Law Function

<p>In this paper, the Bayesian estimation for the unknown parameters for the bivariate generalized exponential (BVGE) distribution under Bivariate censoring type-I samples with constant stress accelerated life testing (CSALT) are discussed. The scale parameter of the lifetime distribution at constant stress levels is assumed to be an inverse power law function of the stress level. The parameters are estimated by Bayesian approach using Markov Chain Monte Carlo (MCMC) method based on Gibbs sampling. Then, the numerical studies are introduced to illustrate the approach study using samples which have been generated from the BVGE distribution.</p>

scholarly journals Statistical Inference for the Weibull Distribution Based on δ-Record Data

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Raúl Gouet ◽  
F. Javier López ◽  
Lina Maldonado ◽  
Gerardo Sanz
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Statistical Inference ◽  
Numerical Computation ◽  
Weibull Model ◽  
Rainfall Series ◽  
Estimation Of Parameters ◽  
Record Data

We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from δ -record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on δ -record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design.

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