Prediction of censored exponential lifetimes in a simple step-stress model under progressive Type II censoring

Indrani Basak, N. Balakrishnan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this article, we consider the problem of predicting survival times of units from the exponential distribution which are censored under a simple step-stress testing experiment. Progressive Type-II censoring are considered for the form of censoring. Two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP)—are derived. Some numerical examples are presented to illustrate the prediction methods developed here. Using simulation studies, prediction intervals are generated for these examples. We then compare the MLP and the CMP with respect to mean squared prediction error and the prediction interval.

Original language English (US) 1665-1687 23 Computational Statistics 32 4 https://doi.org/10.1007/s00180-016-0684-0 Published - Dec 1 2017

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Progressive Type-II Censoring
Predictors
Prediction Interval
Prediction
Maximum likelihood
Maximum Likelihood
Survival Time
Prediction Error
Censoring
Exponential distribution
Mean Squared Error
Model
Simulation Study
Numerical Examples
Testing
Unit
Experiment
Experiments

All Science Journal Classification (ASJC) codes

• Statistics and Probability
• Statistics, Probability and Uncertainty
• Computational Mathematics

Cite this

title = "Prediction of censored exponential lifetimes in a simple step-stress model under progressive Type II censoring",
abstract = "In this article, we consider the problem of predicting survival times of units from the exponential distribution which are censored under a simple step-stress testing experiment. Progressive Type-II censoring are considered for the form of censoring. Two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP)—are derived. Some numerical examples are presented to illustrate the prediction methods developed here. Using simulation studies, prediction intervals are generated for these examples. We then compare the MLP and the CMP with respect to mean squared prediction error and the prediction interval.",
author = "Indrani Basak and N. Balakrishnan",
year = "2017",
month = "12",
day = "1",
doi = "10.1007/s00180-016-0684-0",
language = "English (US)",
volume = "32",
pages = "1665--1687",
journal = "Computational Statistics",
issn = "0943-4062",
publisher = "Springer Verlag",
number = "4",

}

In: Computational Statistics, Vol. 32, No. 4, 01.12.2017, p. 1665-1687.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of censored exponential lifetimes in a simple step-stress model under progressive Type II censoring

AU - Basak, Indrani

AU - Balakrishnan, N.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - In this article, we consider the problem of predicting survival times of units from the exponential distribution which are censored under a simple step-stress testing experiment. Progressive Type-II censoring are considered for the form of censoring. Two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP)—are derived. Some numerical examples are presented to illustrate the prediction methods developed here. Using simulation studies, prediction intervals are generated for these examples. We then compare the MLP and the CMP with respect to mean squared prediction error and the prediction interval.

AB - In this article, we consider the problem of predicting survival times of units from the exponential distribution which are censored under a simple step-stress testing experiment. Progressive Type-II censoring are considered for the form of censoring. Two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP)—are derived. Some numerical examples are presented to illustrate the prediction methods developed here. Using simulation studies, prediction intervals are generated for these examples. We then compare the MLP and the CMP with respect to mean squared prediction error and the prediction interval.

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UR - http://www.scopus.com/inward/citedby.url?scp=84987678268&partnerID=8YFLogxK

U2 - 10.1007/s00180-016-0684-0

DO - 10.1007/s00180-016-0684-0

M3 - Article

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SP - 1665

EP - 1687

JO - Computational Statistics

JF - Computational Statistics

SN - 0943-4062

IS - 4

ER -