Inference for the extreme value distribution under progressive Type-II censoring

Balakrishnan, N.; Kannan, Nandini; Lin, Chien‐Tai; Wu, Shuo‐Jye

doi:10.1080/0094965031000105881

Cited by 101 publications

(49 citation statements)

References 13 publications

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“…Progressive Type-II censoring schemes for various lifetime distributions was discussed by Cohen (1963), who introduced progressive Type-II censoring schemes. Mann (1969Mann ( , 1971, Balakrishnan, Kannan, Lin, and Ng (2003), Balakrishnan, Kannan, Lin, and Wu (2004), Ng (2005), and Ng, Kundu, and Balakrishnan (2006) discussed inference for different lifetime distributions under progressive Type-II censoring schemes. Balakrishnan and Aggarwala (2000) is an excellent reference on progressive censoring.…”

Section: Potdar and Shirke 325mentioning

confidence: 99%

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Potdar¹,

Shirke

2017

J. Mod. App. Stat. Meth.

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Confidence interval construction for the scale parameter of the half-logistic distribution is considered using four different methods. The first two are based on the asymptotic distribution of the maximum likelihood estimator (MLE) and log-transformed MLE. The last two are based on pivotal quantity and generalized pivotal quantity, respectively. The MLE for the scale parameter is obtained using the expectation-maximization (EM) algorithm. Performances are compared with the confidence intervals proposed by Balakrishnan and Asgharzadeh via coverage probabilities, length, and coverage-to-length ratio. Simulation results support the efficacy of the proposed approach.

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Section: Potdar and Shirke 325mentioning

confidence: 99%

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Potdar¹,

Shirke

2017

J. Mod. App. Stat. Meth.

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“…Many authors (e.g., Balakrishnan and Kannan, 2001;Balakrishnan et al, 2004;Alaboud, 2009) used the data in Nelson (1982), which represents log failure times to breakdown of an insulating fluid testing experiment (see Table 4.1). Kang and Seo (2011) use the data for the Kolmogorov test to examine whether the data follow an exponentiated halflogistic distribution when the shape parameter λ = 1.…”

Section: Real Datamentioning

confidence: 99%

Bayesian analysis of an exponentiated half-logistic distribution under progressively type-II censoring

Kang¹,

Seo²,

Kim

2013

Journal of the Korean Data and Information Science Society

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This paper develops maximum likelihood estimators (MLEs) of unknown parameters in an exponentiated half-logistic distribution based on a progressively type-II censored sample. We obtain approximate confidence intervals for the MLEs by using asymptotic variance and covariance matrices. Using importance sampling, we obtain Bayes estimators and corresponding credible intervals with the highest posterior density and Bayes predictive intervals for unknown parameters based on progressively type-II censored data from an exponentiated half logistic distribution. For illustration purposes, we examine the validity of the proposed estimation method by using real and simulated data.

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“…Maswadah (2003) derived the conditional confidence intervals for the parameters based on the generalized order statistics. Balakrishnan et al (2004) discussed point and interval estimation for the extreme value distribution under progressively type-II censoring. Balakrishnan and Kateri (2008) proposed a simple graphical solution for the determination of the maximum likelihood estimator of the shape parameter in the Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Goodness-of-fit test for the logistic distribution based on multiply type-II censored samples

Kang¹,

Han²,

Cho³

2014

Journal of the Korean Data and Information Science Society

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In this paper, we derive the estimators of the location parameter and the scale parameter in a logistic distribution based on multiply type-II censored samples by the approximate maximum likelihood estimation method. We use four modified empirical distribution function (EDF) types test for the logistic distribution based on multiply type-II censored samples using proposed approximate maximum likelihood estimators. We also propose the modified normalized sample Lorenz curve plot for the logistic distribution based on multiply type-II censored samples. For each test, Monte Carlo techniques are used to generate the critical values. The powers of these tests are also investigated under several alternative distributions.

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Inference for the extreme value distribution under progressive Type-II censoring

Cited by 101 publications

References 13 publications

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Bayesian analysis of an exponentiated half-logistic distribution under progressively type-II censoring

Goodness-of-fit test for the logistic distribution based on multiply type-II censored samples

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