Point and interval estimation for Gaussian distribution, based on progressively type-II censored samples

Balakrishnan, N.; Kannan, Nandini; Lin, Chien‐Tai; Ng, Hon Keung Tony

doi:10.1109/tr.2002.805786

Cited by 116 publications

(52 citation statements)

References 10 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%

“…Balakrishnan and Wong (1991) computed the approximate Maximum Likelihood Estimator (AMLE) for the location and scale parameters of the half-logistic distribution. Balakrishnan and Chan (1992) studied estimation for the scale parameter of the half-logistic distribution. Kim and Han (2010) used importance sampling methods to obtain a Bayes estimator for the scale parameter of the half-logistic distribution under progressively Type-II censored samples.…”

Section: Potdar and Shirke 325mentioning

confidence: 99%

See 1 more Smart Citation

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%

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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show abstract

“…Inference, sampling design and generalization based on progressively censored samples were studied by many authors, see among others, Balasooriya et al [3], Ng et al [4], Balakrishnan et al [5], Fernandez [6], Soliman [7], Asgharzadeh [8], Ku and Kaya [9], Wu et al [10], Banerjee and Kundu [11] and Raqab et al [12]. However, a little work is introduced in the Bayesian context.…”

Section: Introductionmentioning

confidence: 99%

Inferences using type-II progressively censored data with binomial removals

et al. 2015

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This paper considers the estimation problem for Burr type-X model, when the lifetimes are collected under type-II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. The methods of maximum likelihood as well as the Bayes procedure to derive both point and interval estimates of the parameters are used. The expected test time to complete the censoring test is computed and analyzed for different censoring schemes. The effect of the binomial distribution parameter p on the expected test time under progressive censoring and the relative expected test time over the complete sample are investigated. Monte Carlo simulations are performed to compare and evaluate the performance of different methods. Furthermore, an example with a real data set is presented for illustrative purposes.

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“…For an excellent discussion on progressive Type-II censoring technique see the monograph of Balakrishnan and Aggarwala 3 and the recent discussion paper by Balakrishnan 4 . There are numerous articles in the literature dealing with inferential procedures based on the progressively Type-II censoring data for a wide variety of lifetime distributions. See for example Mann 5,6 , Balakrishnan et al 7 , Balakrishnan and Kannan 8 , Balakrishnan et al 9 , Kim and Han 10 , Lin et al 11 , and Asgharzadeh 12 among others who study the inferential procedures for the Weibull, extreme value, logistic, normal, half-logistic, loggamma, and generalized exponential models, respectively. GE-exponential distribution was introduced by Marshal and Olkin 13 and further studied extensively by Adamidis and Loukas 14 and Adamidis et al 15 .…”

Section: Introductionmentioning

confidence: 99%

Inference for the Geometric Extreme Exponential Distribution under Progressive Type II Censoring

Pakyari

2012

ISRN Probability and Statistics

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Geometric extreme exponential GE-exponential is one of the nonnegative right-skewed distribution that is suitable for analyzing lifetime data. It is well known that the maximum likelihood estimators MLEs of the parameters lead to likelihood equations that have to be solved numerically. In this paper, we provide explicit estimators through an approximation of the likelihood equations based on progressively Type-II-censored samples. The approximate estimators are then used as starting values to find the MLEs numerically. The bias and variances of the MLEs are calculated for a wide range of sample sizes and different progressive censoring schemes through a Monte Carlo simulation study. Moreover, formulas for the observed Fisher information are given which could be used to construct asymptotic confidence intervals. The coverage probabilities of the confidence intervals and the percentage points of pivotal quantities associated with the MLEs are also calculated. A real dataset has been studied for illustrative purposes.

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Point and interval estimation for Gaussian distribution, based on progressively type-II censored samples

Cited by 116 publications

References 10 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

Inferences using type-II progressively censored data with binomial removals

Inference for the Geometric Extreme Exponential Distribution under Progressive Type II Censoring

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