Estimation of Entropy for Generalized Exponential Distribution Based on Record Values

Chacko, Manoj; Asha, P S

doi:10.1007/s41096-018-0033-4

Cited by 17 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The respective Bayes estimates with respect to the BLL and BEL can be obtained after replacing the desisted Bayes estimates in (4.15)-(4.17). For some recent references in this direction, we may refer to Sultan et al (2014), Kundu and Raqab (2015) and Chacko and Asha (2018).…”

Section: Importance Sampling Methodsmentioning

confidence: 99%

Estimation for the generalized Fréchet distribution under progressive censoring scheme

Maiti

Kayal

2019

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

This paper takes into account the estimation of the unknown model parameters and the reliability characteristics for a generalized Fre ´chet distribution under progressive type-II censored sample. Maximum likelihood estimates are obtained. We calculate asymptotic confidence intervals and also compare in terms of their coverage probabilities for the unknown parameters, and the reliability and hazard rate functions. Further, Bayes estimates are derived with respect to various balanced type symmetric as well as asymmetric loss functions. Note that it is impossible to get the estimates in closed-form. So, we adopt importance sampling method in computing the Bayes estimates. Furthermore, we consider one-sample and twosample prediction problems under Bayesian framework. In this case, the appropriate predictive intervals are proposed. Monte Carlo simulations are performed to observe the performance of various estimates. Moreover, results from simulation studies indicating the performance of the proposed methods are included. Finally, a real dataset is considered and analyzed for the illustration of the inferential procedures studied in this paper.

show abstract

Section: Importance Sampling Methodsmentioning

confidence: 99%

Estimation for the generalized Fréchet distribution under progressive censoring scheme

Maiti

Kayal

2019

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

show abstract

“…In this section we carry out a simulation study for illustrating the estimation procedures developed in Sections 2 and 3. First we obtain the MLEs for Shannon entropy and Renyi entropy using ( 14) and (15). We have obtained the bias and MSE of MLEs for different values of n using 500 simulated samples for different combinations of and and are given in Tables 1 and 2.…”

Section: Simulation Studymentioning

confidence: 99%

“…Baratpour et al [14] developed the entropy of upper record values and provided several upper and lower bounds for this entropy by using the hazard rate function. Chacko and Asha [15] considered the problem of estimation of entropy for generalized exponential distribution using lower record values.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Entropy for Weibull Distribution Based on Record Values

Chacko¹,

Asha²

2021

JSTA

Self Cite

View full text Add to dashboard Cite

In this paper, estimation of entropy for Weibull distribution based on record values is considered. Maximum likelihood estimation and Bayes estimation for Shannon entropy and Renyi entropy have been considered based on record values. Bayes estimators are obtained using Markov Chain Monte Carlo (MCMC) method. A simulation study is performed to find the performance of the estimators developed in this paper. The inferential procedures developed in this paper have also been illustrated using real data.

show abstract

“…In recent times, several authors studied the statistical inferences for the entropy measures using different distributions and sampling schemes (for example, [4][5][6][7][8][9][10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Dynamic Cumulative Residual Entropy for Lindley Distribution

Almarashi

Algarni

Hassan

et al. 2021

Entropy

View full text Add to dashboard Cite

Dynamic cumulative residual (DCR) entropy is a valuable randomness metric that may be used in survival analysis. The Bayesian estimator of the DCR Rényi entropy (DCRRéE) for the Lindley distribution using the gamma prior is discussed in this article. Using a number of selective loss functions, the Bayesian estimator and the Bayesian credible interval are calculated. In order to compare the theoretical results, a Monte Carlo simulation experiment is proposed. Generally, we note that for a small true value of the DCRRéE, the Bayesian estimates under the linear exponential loss function are favorable compared to the others based on this simulation study. Furthermore, for large true values of the DCRRéE, the Bayesian estimate under the precautionary loss function is more suitable than the others. The Bayesian estimates of the DCRRéE work well when increasing the sample size. Real-world data is evaluated for further clarification, allowing the theoretical results to be validated.

show abstract

Estimation of Entropy for Generalized Exponential Distribution Based on Record Values

Cited by 17 publications

References 19 publications

Estimation for the generalized Fréchet distribution under progressive censoring scheme

Estimation for the generalized Fréchet distribution under progressive censoring scheme

Estimation of Entropy for Weibull Distribution Based on Record Values

Bayesian Analysis of Dynamic Cumulative Residual Entropy for Lindley Distribution

Contact Info

Product

Resources

About