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 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.

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 EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2483
Author(s):  
Tzong-Ru Tsai ◽  
Yuhlong Lio ◽  
Wei-Chen Ting
Keyword(s):  
Expectation Maximization ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Mixture Distributions ◽  
Type I ◽  
Censored Samples ◽  
Censoring Scheme

An expectation–maximization (EM) likelihood estimation procedure is proposed to obtain the maximum likelihood estimates of the parameters in a mixture distributions model based on type-I hybrid censored samples when the mixture proportions are unknown. Three bootstrap methods are applied to construct the confidence intervals of the model parameters. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results show that the proposed methods can perform well to obtain reliable point and interval estimation results. Three examples are used to illustrate the applications of the proposed methods.

scholarly journals Statistical Analysis of Joint Type-I Generalized Hybrid Censoring Data from Burr XII Lifetime Distributions

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mahmoud Ragab ◽  
Aisha Fayomi ◽  
Ali Algarni ◽  
G. A. Abd-Elmougod ◽  
Neveen Sayed-Ahmed ◽  
...  
Keyword(s):  
Model Parameters ◽  
Type I ◽  
Bayes Method ◽  
Monte Carlo Simulation Study ◽  
Lifetime Distributions ◽  
Hybrid Censoring ◽  
Life Tests ◽  
Censoring Scheme ◽  
Theoretical Results

The quality of the products coming from different lines of production requires some tests called comparative life tests. For lines having the same facility, the lifetime of the product is distributed by Burr XII, the lifetime distribution, and units are tested under type-I generalized hybrid censoring scheme. The observed censoring data are used under maximum likelihood and the Bayes method to estimate the model parameters. The theoretical results are discussed and assessed through data analysis and Monte Carlo simulation study. Finally, we reported some brief comments obtained from numerical computation.

Minimum-Distance Parametric Estimation Under Progressive Type-I Censoring

2010 ◽  
Vol 59 (2) ◽  
pp. 413-425 ◽  
Author(s):  
Narayanaswamy Balakrishnan ◽  
Laurent Bordes ◽  
Xuejing Zhao
Keyword(s):  
Minimum Distance ◽  
Parametric Estimation ◽  
Type I ◽  
Type I Censoring ◽  
Progressive Type

scholarly journals A Study on A New Type 1 Half-Logistic Family of Distributions and Its Applications

2020 ◽  
Vol 8 (4) ◽  
pp. 934-949
Author(s):  
Morad Alizadeh ◽  
Alireza Nematollahi ◽  
Emrah Altun ◽  
Mahdi Rasekhi
Keyword(s):  
Residual Life ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Mean Deviation ◽  
Model Parameters ◽  
Type I ◽  
Monte Carlo Simulation Study ◽  
New Type ◽  
Application Fields ◽  
Family Of Distributions

In this paper, we propose a new class of continuous distributions with two extra shape parameters called the a new type I half logistic-G family of distributions. Some of important properties including ordinary moments, quantiles, moment generating function, mean deviation, moment of residual life, moment of reversed residual life, order statistics and extreme value are obtained. To estimate the model parameters, the maximum likelihood method is also applied by means of Monte Carlo simulation study. A new location-scale regression model based on the new type I half logistic-Weibull distribution is then introduced. Applications of the proposed family is demonstrated in many fields such as survival analysis and univariate data fitting. Empirical results show that the proposed models provide better fits than other well-known classes of distributions in many application fields.

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
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