Inferences for multiple interval type-I censoring scheme

2021 ◽  
pp. 1-20
Author(s):  
Shubham Agnihotri ◽  
Sanjay Kumar Singh ◽  
Umesh Singh
Keyword(s):  
Type I ◽  
Type I Censoring ◽  
Interval Type ◽  
Censoring Scheme

scholarly journals A Progressive Interval Type-I Censored Life Test Plan for Rayleigh Distribution

2019 ◽  
Vol 48 (3) ◽  
pp. 76-86
Author(s):  
Arun Kaushik
Keyword(s):  
Rayleigh Distribution ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Type I ◽  
Test Plan ◽  
Type I Censoring ◽  
Progressive Type ◽  
Interval Type ◽  
Censoring Scheme ◽  
Two Parameter

In this paper, we have considered the problem of optimal inspection times for the progressive interval type-I censoring scheme where uncertainty in the process is governed by the two-parameter Rayleigh distribution. Here, we also introduced some optimality criterion and determined the optimum inspection times, accordingly. The effect of the number of inspections and choice of optimally spaced inspection times based on the asymptotic relative efficiencies of the maximum likelihood estimates of the parameters are also investigated. Further, we have discussed the optimal progressive type-I interval censoring plan when the inspection times and the expected proportions of total failures in the experiment are under control.

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.

Bayesian inference using product of spacings function for Progressive hybrid Type-I censoring scheme

Statistics ◽  
2017 ◽  
Vol 52 (2) ◽  
pp. 345-363 ◽  
Author(s):  
Suparna Basu ◽  
Sanjay K. Singh ◽  
Umesh Singh
Keyword(s):  
Bayesian Inference ◽  
Type I ◽  
Type I Censoring ◽  
Hybrid Type ◽  
Censoring Scheme

scholarly journals Balanced Joint Progressively Hybrid Type-I Censoring Samples in Estimating the Lifetime Lomax Distributions

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nada M. Alfaer ◽  
Hassan M. Aljohani
Keyword(s):  
Likelihood Estimation ◽  
Model Parameters ◽  
Type I ◽  
Monte Carlo Simulation Study ◽  
Type I Censoring ◽  
Hybrid Type ◽  
Progressive Type ◽  
Estimation Problems ◽  
Credible Intervals ◽  
Censoring Scheme

The comparative life testing for products from different production lines under joint censoring schemes has received some attention over the past few years. Mondal and Kundu recently used the balanced joint progressive type-II censoring scheme to discuss the comparative exponential and Weibull populations. This paper implements the balanced censoring scheme with a hybrid progressive type-I censoring scheme known as a balanced joint progressive hybrid type-I censoring scheme (BJPHCS). The life Lomax products’ model formulation from two different lines of production with BJPHCS is discussed. The model parameters are estimated under maximum likelihood estimation for point and the corresponding asymptotic confidence intervals. Under independent gamma priors, the Bayes estimators and associated credible intervals are obtained with the help of MCMC technique. The validity of the theoretical results developed in this paper for estimation problems is discussed through numerical example and Monte Carlo simulation study, which report the estimators’ quality. Finally, we give a brief comment describing the numerical results.

scholarly journals E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 636 ◽  
Author(s):  
Ali Algarni ◽  
Abdullah M. Almarashi ◽  
Hassan Okasha ◽  
Hon Keung Tony Ng
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Scale Parameter ◽  
Type I ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Type I Censoring ◽  
Squared Error ◽  
Censoring Scheme ◽  
Bayesian Estimators

In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamma distribution as a conjugate prior for the unknown scale parameter. Also, the E-Bayesian estimators are derived using three different distributions for the hyper-parameters. Some properties of E-Bayesian estimators based on balanced squared error loss function are discussed. A simulation study is performed to compare the efficiencies of different estimators in terms of minimum mean squared errors. Finally, a real data set is analyzed to illustrate the applicability of the proposed estimators.

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