Entropy properties of record statistics

Baratpour, S.; Ahmadi, Jafar; Arghami, Nasser Reza

doi:10.1007/s00362-006-0326-7

Cited by 60 publications

(31 citation statements)

References 18 publications

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“…Baratpour et al [2] obtained the entropy of a continuous probability distribution using upper record values. Moreover, they obtained several bounds for this entropy using the hazard rate function.…”

Section: Introductionmentioning

confidence: 99%

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

Seo

Kim

2017

Entropy

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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.

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Section: Introductionmentioning

confidence: 99%

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

Seo

Kim

2017

Entropy

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“…While system's lifetime is computed according to max{U 1,1 ,...,U m,m }, it will be appropriate to know each U i,i to acquire the entire lifetime system. The information measures for record values have been investigated by several authors, including, Zahedi and Shakil (2006), Baratpour et al (2007), and Madadi and Tata (2011). Recently, Jafari Jozani and Ahmadi (2014) studied uncertainty and information properties of RSS.…”

Section: Shannon Entropy Of Rrssmentioning

confidence: 99%

Information Measures for Record Ranked Set Samples

Eskandarzadeh¹,

Tahmasebi

Afshari³

2016

CeN

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Salehi and Ahmadi (2014) introduced a new sampling scheme for generating record-breaking data called record ranked set sampling. In this paper, we consider the uncertainty and information content of record ranked set samples (RRSS) in terms of Shannon entropy, Rényi and Kullback-Leibler (KL) information measures. We show that the difference between the Shannon entropy of RRSS and the simple random samples (SRS) is depends on the parent distribution F. We also compare the information content of RRSS with a SRS data in the uniform

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“…Many authors worked on the estimation entropy for different life distributions. Baratpour et al [4] developed the entropy of upper record values and provided several upper and lower bounds for this entropy by using the hazard rate function. Cramer and Bagh [5] discussed the entropy in the Weibull distribution for progressive censoring.…”

Section: Introductionmentioning

confidence: 99%

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

Cho

Sun

Lee

2015

Entropy

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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.

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Entropy properties of record statistics

Abstract: Record values, Shannon information, Hazard rate function, Fisher information, Mutual information,

Cited by 60 publications

References 18 publications

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

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

Information Measures for Record Ranked Set Samples

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

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