Estimation for the two-parameter pareto distribution under progressive censoring with uniform removals

Wu, Shuo‐Jye

doi:10.1080/00949650215732

Cited by 41 publications

(20 citation statements)

References 17 publications

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“…This is what this paper attempts, by taking this scheme and applying it, with some modifications, for the purpose of actuarial risk modeling and analysis. [11][12][13][14][15] investigated the progressive type II censoring in life testing applications with random removals. The common features of their works are the following: n is fixed, m is preassigned, when any individual item is removed at the i-th failure from the set of items in the test independent of the others but with the same probability p, R i items are randomly removed from the test, and these removals follow a Binomial distribution.…”

Section: The Scheme Of Progressive Type II Censoringmentioning

confidence: 99%

Risk analysis under progressive type II censoring with binomial claim numbers

Kemaloglu

Gebizlioğlu

2009

Journal of Computational and Applied Mathematics

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a b s t r a c tThis paper presents a model of actuarial loss events that follow a progressive censoring scheme. Loss events are modelled according to this scheme regarding the claim number and size. Claim events at random time points are assumed to happen progressively in a given period due to each of an m number of claims that occur due to hazardous events, while a fixed number of n claims are anticipated to take place in total. Distribution of the resulting total loss amount is derived, and according to its properties, some risk management issues about reserves and solvency are discussed.

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Section: The Scheme Of Progressive Type II Censoringmentioning

confidence: 99%

Risk analysis under progressive type II censoring with binomial claim numbers

Kemaloglu

Gebizlioğlu

2009

Journal of Computational and Applied Mathematics

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“…In a life testing experiment, it is beneficial to have an idea about the expected duration of the experiment. Under progressive censoring with random removals, expected time on test (ETT) was computed by Yuen and Tse [13] for Weibull distribution and by Wu [14] for Pareto distribution. For random censoring, ETT has not been computed in the literature so far.…”

Section: Expected Time On Testmentioning

confidence: 99%

Estimation in Maxwell distribution with randomly censored data

Krishna

Vivekanand

Kumar

2014

Journal of Statistical Computation and Simulation

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In many practical situations, complete data are not available in lifetime studies. Many of the available observations are right censored giving survival information up to a noted time and not the exact failure times. This constitutes randomly censored data. In this paper, we consider Maxwell distribution as a survival time model. The censoring time is also assumed to follow a Maxwell distribution with a different parameter. Maximum likelihood estimators and confidence intervals for the parameters are derived with randomly censored data. Bayes estimators are also developed with inverted gamma priors and generalized entropy loss function. A Monte Carlo simulation study is performed to compare the developed estimation procedures. A real data example is given at the end of the study.

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“…When data are obtained by progressive censoring, inference problems for various distributions have been studied by several authors including Wong [13], Yuen and Tse [14], Balasooriya and Saw [15], Balakrishnan and Lin [16], and Wu [17].…”

Section: Progressively Type II Censored Datamentioning

confidence: 99%

Bayesian inference for Rayleigh distribution under progressive censored sample

Chen

2006

Appl. Stochastic Models Bus. Ind.

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SUMMARYIt is often the case that some information is available on the parameter of failure time distributions from previous experiments or analyses of failure time data. The Bayesian approach provides the methodology for incorporation of previous information with the current data. In this paper, given a progressively type II censored sample from a Rayleigh distribution, Bayesian estimators and credible intervals are obtained for the parameter and reliability function. We also derive the Bayes predictive estimator and highest posterior density prediction interval for future observations. Two numerical examples are presented for illustration and some simulation study and comparisons are performed.

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Estimation for the two-parameter pareto distribution under progressive censoring with uniform removals

Cited by 41 publications

References 17 publications

Risk analysis under progressive type II censoring with binomial claim numbers

Risk analysis under progressive type II censoring with binomial claim numbers

Estimation in Maxwell distribution with randomly censored data

Bayesian inference for Rayleigh distribution under progressive censored sample

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