Progressive Type-II Censoring Research Articles

Asymmetric Power Hazard Distribution for COVID-19 Mortality Rate under Adaptive Type-II Progressive Censoring: Theory and Inferences.

This article investigates the estimation of the parameters for power hazard function distribution and some lifetime indices such as reliability function, hazard rate function, and coefficient of variation based on adaptive Type-II progressive censoring. From the perspective of frequentism, we derive the point estimations through the method of maximum likelihood estimation. Besides, delta method is implemented to construct the variances of the reliability characteristics. Markov chain Monte Carlo techniques are proposed to construct the Bayes estimates. To this end, the results of the Bayes estimates are obtained under squared error and linear exponential loss functions. Also, the corresponding credible intervals are constructed. A simulation study is utilized to assay the performance of the proposed methods. Finally, a real data set of COVID-19 mortality rate is analyzed to validate the introduced inference methods.

Assessing the Lifetime Performance Index with Digital Inferences of Power Hazard Function Distribution Using Progressive Type-II Censoring Scheme.

This paper deals with estimating the lifetime performance index. The maximum likelihood (ML) and Bayesian estimators for lifetime performance index CLX where LX is the lower specification limit are derived based on progressive type-II censored (Prog-Type-II-C) sample from two-parameter power hazard function distribution (PHFD). Knowing the lower specification limit, the MLE of CLX is applied to construct a new hypothesis testing procedure. Bayesian estimator of CLX is also utilized to develop a credible interval. Also, the relationship between the CLX and the conforming rate of products is investigated. Moreover, the Bayesian test to evaluate the lifetime performance of units is proposed. A simulation study and illustrative example based on a real dataset are discussed to evaluate the performance of the two tests.

Estimation to the Parameters of Truncated Weibull Rayleigh Distribution under Progressive type-II Censoring Scheme with an application to lifetime data

Estimation to the Parameters of Truncated Weibull Rayleigh Distribution under Progressive type-II Censoring Scheme with an application to lifetime data

Constant-Partially Accelerated Life Tests for Three- Parameter Distribution: Bayes Inference using Progressive Type-II Censoring

Constant-Partially Accelerated Life Tests for Three- Parameter Distribution: Bayes Inference using Progressive Type-II Censoring

Inferences for two Weibull Fre ́chet Populations under Joint Progressive Type-II Censoring with Applications in Engineering Chemistry

Inferences for two Weibull Fre ́chet Populations under Joint Progressive Type-II Censoring with Applications in Engineering Chemistry

Classical and Bayesian inferences for two Topp-Leone models under joint progressive Type-II censoring

Recently, the progressive Type-II censoring has been extended to a more general censoring scheme, called joint progressive Type-II censoring, which studies the lifetimes of two or more populations simultaneously. In this article, we consider the joint Type-II progressive censoring scheme for two populations when their lifetimes follow Topp-Leone models with unknown common scale parameter but different shape parameters. Classical and Bayesian inferences are studied. Expectation-Maximization (EM) algorithm is implemented for obtaining the maximum likelihood estimators (MLEs) and the associated asymptotic confidence intervals of the unknown parameters. Bayesian inferences are discussed based on a beta-gamma prior for the shape parameters and an incomplete inverse gamma prior for the scale parameter. Importance sampling method is proposed to approximate the Bayes estimates. The associated Bayesian credible intervals are also established. Monte Carlo simulation study is performed to compare the performance of the proposed methods. Finally, a real data set representing two different algorithms for estimating unit capacity factors is analyzed for illustrative purposes.

Partially constant-stress accelerated life tests model for parameters estimation of Kumaraswamy distribution under adaptive Type-II progressive censoring

In a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time. The problem of statistical inference of constant-stress ALTs based on censored data is discussed in this paper. So, we implement partially constant-stress ALTs model to test units have two parameter Kumaraswamy lifetime population under adaptive Type-II progressive censoring scheme. The population parameters as well as acceleration factor are estimated by using maximum likelihood method for point and interval estimation. Two different confidence intervals are obtained under bootstrap technique. Also, Bayesian approach under different loss functions is used to contract the point and interval estimates of the model parameters with the help of Markov chain Monte Carlo method (MCMC). For illustrative purpose a simulate data set are analyzed. Different developed results discussed in this paper are compared through Monte Carlo simulation study.

On two exponential populations under a joint adaptive type-II progressive censoring

In this paper, we introduce a new joint adaptive Type-II progressive censoring (JAPC) scheme for independent samples from two different populations. We place two independent samples simultaneously on a life testing experiment. It is assumed that the lifetime of the experimental units of the populations follow exponential distribution with mean and , respectively. The maximum likelihood estimators of the unknown parameters and their exact distributions are derived. Based on the exact distributions of the maximum likelihood estimators, approximate confidence intervals are constructed. Further, the Bayesian inference of the model parameters is considered under a very flexible Beta-Gamma prior. We obtain Bayes estimators and associated credible intervals of the unknown parameters under squared error loss function. Extensive simulations are performed to see the effectiveness of the proposed estimation methods. A real dataset is considered for implementing the proposed model on it. Also we use the variable neighbourhood search (VNS) method to derive the optimal censoring scheme of the model in the Bayesian framework.

Bayesian Approximation for a Mixture Weibull and Lomax Distributions based on Progressive Type-II Censoring Scheme

Bayesian Approximation for a Mixture Weibull and Lomax Distributions based on Progressive Type-II Censoring Scheme

A new statistical inference method for multi-stress accelerated life testing based on random variable transformation

The limited sample size in multi-stress accelerated life testing makes the conventional large-sample-based inference methods inefficient. To overcome this problem, this paper develops a new statistical inference method based on random variable transformation for multi-stress accelerated life testing with Weibull distribution and progressive Type-II censoring. Firstly, a χ2 statistic is constructed using the random variable transformation method, and the exact point estimates of model parameters are derived based on the χ2 statistic. Subsequently, based on the χ2 statistic, the exact confidence interval of shape parameter is provided, while the generalized confidence intervals of the accelerated model parameters are calculated by constructing the new multivariate generalized pivotal quantities. Furthermore, the generalized confidence intervals of some commonly used reliability indexes are provided for better reliability management and decision making. Finally, a simulation study and a real case study are conducted to illustrate the implementation and effectiveness of the proposed method.

On Progressive Censored Competing Risks Data: Real Data Application and Simulation Study

Competing risks are frequently overlooked, and the event of interest is analyzed with conventional statistical techniques. In this article, we consider the analysis of bi-causes of failure in the context of competing risk models using the extension of the exponential distribution under progressive Type-II censoring. Maximum likelihood estimates for the unknown parameters via the expectation-maximization algorithm are obtained. Moreover, the Bayes estimates of the unknown parameters are approximated using Tierney-Kadane and MCMC techniques. Interval estimates using Bayesian and classical techniques are also considered. Two real data sets are investigated to illustrate the different estimation methods, and to compare the suggested model with Weibull distribution. Furthermore, the estimation methods are compared through a comprehensive simulation study.

Estimation of Stress-Strength Reliability for Marshall-Olkin Extended Weibull Family Based on Type-II Progressive Censoring

Estimation of Stress-Strength Reliability for Marshall-Olkin Extended Weibull Family Based on Type-II Progressive Censoring

Bayesian and Non-Bayesian Estimation for the Parameter of Bivariate Generalized Rayleigh Distribution Based on Clayton Copula under Progressive Type-II Censoring with Random Removal

Bayesian and Non-Bayesian Estimation for the Parameter of Bivariate Generalized Rayleigh Distribution Based on Clayton Copula under Progressive Type-II Censoring with Random Removal

Mixed Weibull distributions for the Bayesian analysis of reliability when failures are progressively censored

The progressive type-II censoring has a wide range of applications in reliability studies and survival analysis. The analysis of the mixture models under progressively type-II censored samples has recently been introduced. The reliability estimation from the mixture model under the optimal progressive censoring schemes is still lacking in the current literature. We have discussed the estimation of parameters and reliability characteristics from the Weibull mixture model when failures are progressively censored. The Weibull mixture is proposed for this case as it is S-shaped, which can appropriately handle the mixture data. We have addressed the problem of determination of optimal censoring schemes and estimation of expected test times using the said mixed model. The optimal censoring schemes were found to be less sensitive hence robust. As the corresponding posterior distributions do not provide explicit solutions, we have considered Lindley’s approximation for the approximate numerical solutions.

Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring

In this paper, we consider the problems of estimating the unknown parameters as well as predicting the failure times of the removed units in multiple stages of the progressively censored sample coming from a new Pareto-type distribution. First, maximum likelihood, least squares and Bayesian methods (Lindley’s approximation and Markov chain Monte Carlo) are applied for estimating the parameters involved in this model. For predicting the failure times of the removed units, different prediction methods including maximum likelihood, best unbiased, conditional median and Bayesian prediction methods are adopted. We also develop prediction intervals of future lifetimes using pivot and highest conditional density methods. Comprehensive Monte Carlo simulations are performed to assess the performance of the estimation methods as well as prediction methods. Finally, analysis of a real data set representing the duration of remission of leukemia patients who were treated by a specific drug is performed for illustration purposes.

Confidence bands for exponential distribution functions under progressive type-II censoring

Based on a progressively type-II censored sample from the exponential distribution with unknown location and scale parameter, confidence bands are proposed for the underlying distribution function by using confidence regions for the parameters and Kolmogorov–Smirnov type statistics. Simple explicit representations for the boundaries and for the coverage probabilities of the confidence bands are analytically derived, and the performance of the bands is compared in terms of band width and area by means of a data example. As a by-product, a novel confidence region for the location-scale parameter is obtained. Extensions of the results to related models for ordered data, such as sequential order statistics, as well as to other underlying location-scale families of distributions are discussed.

Information Geometry of the Exponential Family of Distributions with Progressive Type-II Censoring.

In geometry and topology, a family of probability distributions can be analyzed as the points on a manifold, known as statistical manifold, with intrinsic coordinates corresponding to the parameters of the distribution. Consider the exponential family of distributions with progressive Type-II censoring as the manifold of a statistical model, we use the information geometry methods to investigate the geometric quantities such as the tangent space, the Fisher metric tensors, the affine connection and the -connection of the manifold. As an application of the geometric quantities, the asymptotic expansions of the posterior density function and the posterior Bayesian predictive density function of the manifold are discussed. The results show that the asymptotic expansions are related to the coefficients of the -connections and metric tensors, and the predictive density function is the estimated density function in an asymptotic sense. The main results are illustrated by considering the Rayleigh distribution.

Inference on Nadarajah–Haghighi distribution with constant stress partially accelerated life tests under progressive type-II censoring

This paper presents methods of estimation of the parameters and acceleration factor for Nadarajah–Haghighi distribution based on constant-stress partially accelerated life tests. Based on progressive Type-II censoring, Maximum likelihood and Bayes estimates of the model parameters and acceleration factor are established, respectively. In addition, approximate confidence interval are constructed via asymptotic variance and covariance matrix, and Bayesian credible intervals are obtained based on importance sampling procedure. For comparison purpose, alternative bootstrap confidence intervals for unknown parameters and acceleration factor are also presented. Finally, extensive simulation studies are conducted for investigating the performance of the our results, and two data sets are analyzed to show the applicabilities of the proposed methods.

Estimation and Prediction for Gompertz Distribution under General Progressive Censoring

In this article, we discuss the estimation of the parameters for Gompertz distribution and prediction using general progressive Type-II censoring. Based on the Expectation–Maximization algorithm, we calculate the maximum likelihood estimates. Bayesian estimates are considered under different loss functions, which are symmetrical, asymmetrical and balanced, respectively. An approximate method—Tierney and Kadane—is used to derive the estimates. Besides, the Metropolis-Hasting (MH) algorithm is applied to get the Bayesian estimates as well. According to Fisher information matrix, we acquire asymptotic confidence intervals. Bootstrap intervals are also established. Furthermore, we build the highest posterior density intervals through the sample generated by the MH algorithm. Then, Bayesian predictive intervals and estimates for future samples are provided. Finally, for evaluating the quality of the approaches, a numerical simulation study is implemented. In addition, we analyze two real datasets.

Kernel and CDF-Based Estimation of Extropy and Entropy from Progressively Type-II Censoring with Application for Goodness of Fit Problems

Abstract Recently, entropy and extropy-based tests for the uniform distribution have attracted the attention of some researchers. This paper proposes nonparametric entropy and extropy estimators based on progressive type-II censoring and investigates their properties and behavior. Performance of the proposed estimators is studied via simulations. Entropy and extropy-based goodness-of-fit tests for uniformity are developed by the well performed estimators. The powers of the proposed uniformity tests are compared also via simulations assuming various alternatives and censoring schemes.