The Entropy of Progressively Censored Samples

Recently, progressive hybrid censoring schemes have become quite popular in a life-testing problem and reliability analysis. However, the limitation of the progressive hybrid censoring scheme is that it cannot be applied when few failures occur before time T . Therefore, a generalized progressive hybrid censoring scheme was introduced. In this paper, the estimation of the entropy of a two-parameter Weibull distribution based on the generalized progressively censored sample has been considered. The Bayes estimators for the entropy of the Weibull distribution based on the symmetric and asymmetric loss functions, such as the squared error, linex and general entropy loss functions, are provided. The Bayes estimators cannot be obtained explicitly, and Lindley's approximation is used to obtain the Bayes estimators. Simulation experiments are performed to see the effectiveness of the different estimators. Finally, a real dataset has been analyzed for illustrative purposes.

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the Entropy of a Weibull Distribution under Generalized Progressive Hybrid Censoring

Cho

Sun

Lee

2015

“…Moreover, they obtained several bounds for this entropy using the hazard rate function. Abo-Eleneen [3] suggested an efficient computation method for the entropy in progressively Type-II censored samples. Kang et al [4], using maximum likelihood estimators (MLE) and approximate MLE (AMLE), derived estimators of the entropy of a double-exponential distribution that are based on multiply Type-II censored samples.…”

Section: Introductionmentioning

confidence: 99%

Objective Bayesian Entropy Inference for Two-Parameter Logistic Distribution Using Upper Record Values

Seo

Kim

2017

Abstract:In this paper, we provide an entropy inference method that is based on an objective Bayesian approach for upper record values having a two-parameter logistic distribution. We derive the entropy that is based on the i-th upper record value and the joint entropy that is based on the upper record values. Moreover, we examine their properties. For objective Bayesian analysis, we obtain objective priors, namely, the Jeffreys and reference priors, for the unknown parameters of the logistic distribution. The priors are based on upper record values. Then, we develop an entropy inference method that is based on these objective priors. In real data analysis, we assess the quality of the proposed models under the objective priors and compare them with the model under the informative prior.

“…Baratpour et al [2] developed the entropy of upper record values and provided several upper and lower bounds for this entropy by using the hazard rate function. Abo-Eleneen [3] suggested an efficient method for the simple computation of entropy in progressively Type-II censored samples. Kang et al [4] derived estimators for the entropy function of a double exponential distribution based on multiply Type-II censored samples by using maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs).…”

Section: Introductionmentioning

confidence: 99%

Entropy Estimation of Generalized Half-Logistic Distribution (GHLD) Based on Type-II Censored Samples

Seo

Kang

2014