• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.93-103
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• 2014

This paper deals with the problem of predicting censored data in a half triangle distribution with an unknown parameter based on progressively Type-II censored samples. We derive maximum likelihood predictors and some approximate maximum likelihood predictors of censored failure times in a progressively Type-II censoring scheme. In addition, we construct the shortest-length predictive intervals for censored failure times. Finally, Monte Carlo simulations are used to assess the validity of the proposed methods.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1455-1464
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• 2013

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.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.657-666
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• 2011

In this paper, we derive the maximum likelihood estimator(MLE) and some approximate maximum likelihood estimators(AMLEs) of the scale parameter in an exponentiated half logistic distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error(MSE) through a Monte Carlo simulation for various censoring schemes. We also obtain the AMLEs of the reliability function.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.99-106
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• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.191-203
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• 2019

The problem of examining how well an assumed distribution fits the data of a sample is of significant and must be examined prior to any inferential process. The observed failure time data of items are often not wholly available in reliability and life-testing studies. Lowering the expense and period associated with tests is important in statistical tests with censored data. Goodness-of-fit tests for perfect data can no longer be used when the observed failure time data are progressive Type II censored (PC) data. Therefore, we propose goodness-of-fit test statistics and a graphical method based on generalized Lorenz curve for PC data from a location-scale distribution. The power of the proposed tests is then assessed through Monte Carlo simulations. Finally, we analyzed two real data set for illustrative purposes.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.487-495
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• 2010

This paper develops a goodness of fit test statistic to test if the progressively Type II censored sample comes from an exponential distribution with origin known. The test is based on normalizing spacings and Stephens (1978)' modified Shapiro and Wilk (1972) test for exponentiality. The modification is for the case where the origin is known. We applied the same modification to Kim (2001a)'s statistic, which is based on the ratio of two asymptotically efficient estimates of scale. The simulation results show that Kim (2001a)'s statistic has higher power than Stephens' modified Shapiro and Wilk statistic for almost all cases.