Estimating the parameters of a Burr distribution under progressive type II censoring

Rastogi, Manoj Kumar; Tripathi, Yogesh Mani

doi:10.1016/j.stamet.2011.10.002

Cited by 44 publications

(16 citation statements)

References 16 publications

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“…Due to such restrictions various asymmetric loss functions are introduced in the literature, such as general entropy loss function. These loss functions have been proven useful for performing Bayesian analysis in different fields of reliability estimation and life testing problems (Rastogi and Tripathi [11]). …”

Section: Bayes Estimationmentioning

confidence: 99%

An Estimation of the Entropy for a Rayleigh Distribution Based on Doubly-Generalized Type-II Hybrid Censored Samples

Cho

Sun

Lee

2014

Entropy

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In this paper, based on a doubly generalized Type II censored sample, the maximum likelihood estimators (MLEs), the approximate MLE and the Bayes estimator for the entropy of the Rayleigh distribution are derived. We compare the entropy estimators' root mean squared error (RMSE), bias and Kullback-Leibler divergence values. The simulation procedure is repeated 10,000 times for the sample size n = 10, 20, 40 and 100 and various doubly generalized Type II hybrid censoring schemes. Finally, a real data set has been analyzed for illustrative purposes.

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Section: Bayes Estimationmentioning

confidence: 99%

An Estimation of the Entropy for a Rayleigh Distribution Based on Doubly-Generalized Type-II Hybrid Censored Samples

Cho

Sun

Lee

2014

Entropy

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“…The BXII was originally proposed by Burr (1942) and received more attention by the researchers due to its broad applications in the study of engineering, reliability, life testing, and several industrial and economic experiments. See for example, Ali Mousa and Jaheen (2002); Wu and Yu (2005); Rastogi and Tripathi (2012). Now, we want to decide which of the two rival models is closer to the true model …”

Section: Discussionmentioning

confidence: 99%

Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples

Sayyareh

Panahi

2018

JIRSS

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Abstract. The aim of statistical modeling is to identify the model that most closely approximates the underlying process. Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation. In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid censoring scheme. Asymptotic properties of maximum likelihood estimator based on the missing information principle are derived. Also, asymptotic distribution of the normalized difference of AICs is obtained and it is used to construct an interval, say tracking interval, for comparing the two competing models. Monte Carlo simulations are performed to examine how the proposed interval works for different censoring schemes. Two real datasets have been analyzed for illustrative purposes. The first is selecting between Weibull and generalized exponential distributions for main component of spearmint essential oil purification data. The second is the choice between models of the lifetimes of 20 electronic components.

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“…Krishna and Kumar [14] discussed the inference problems in Lindley distribution and the results shows that Lindley distribution provide good parametric fit under progressive censoring scheme for some real life situations. Also, some of the recent work on progressive censoring include but not limited to Kumar et al [15], Pak et al [16] and Rastogi and Tripathi [17]. As far as we know, no one has described the EM algorithm for determining the MLEs of the parameters of the Poisson-Exponential distribution based on progressive type-II censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Parameters of Poisson-Exponential Distribution Based on Progressively Type II Censoring Using the Expectation Maximization (Em) Algorithm

Gitahi¹

2017

AJTAS

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This paper considers the parameter estimation problem of test units from Poisson-Exponential distribution based on progressively type II right censoring scheme. The maximum likelihood estimators (MLEs) for Poisson-Exponential parameters are derived using Expectation Maximization (EM) algorithm. EM-algorithm is also used to obtain the estimates as well as the asymptotic variance-covariance matrix. By using the obtained variance-covariance matrix of the MLEs, the asymptotic 95% confidence interval for the parameters are constructed. Through simulation, the behavior of these estimates are studied and compared under different censoring schemes and parameter values. It is concluded that for an increasing sample size; the estimated value of the parameters converges to the true value, the variances decrease and the width of the confidence interval become narrower.

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Estimating the parameters of a Burr distribution under progressive type II censoring

Cited by 44 publications

References 16 publications

An Estimation of the Entropy for a Rayleigh Distribution Based on Doubly-Generalized Type-II Hybrid Censored Samples

An Estimation of the Entropy for a Rayleigh Distribution Based on Doubly-Generalized Type-II Hybrid Censored Samples

Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples

Estimation of the Parameters of Poisson-Exponential Distribution Based on Progressively Type II Censoring Using the Expectation Maximization (Em) Algorithm

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