Nonparametric estimation of mean residual lifetime in ranked set sampling with a concomitant variable

The mean residual lifetime (MRL) of a unit is its expected additional lifetime provided that it has survived until time t. The MRL estimation problem has been frequently addressed in the literature since it has wide applications in statistics, reliability and survival analysis. In this paper, we con...

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Bibliographic Details
Published inJournal of applied statistics Vol. 51; no. 13; pp. 2512 - 2528
Main Authors Zamanzade, Ehsan, Mahdizadeh, M., Samawi, Hani M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.10.2024
Taylor & Francis Ltd
Subjects
Estimation
Judgment ranking
Monte Carlo simulation
nonparametric regression
Random sampling
ranked set sampling
relative efficiency
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Summary:The mean residual lifetime (MRL) of a unit is its expected additional lifetime provided that it has survived until time t. The MRL estimation problem has been frequently addressed in the literature since it has wide applications in statistics, reliability and survival analysis. In this paper, we consider the problem of estimating the MRL in ranked set sampling when actual quantifications of a concomitant variable are available. To exploit the additional information of the concomitant variable, we introduce several MRL estimators based on some regression techniques. We then compare them with the standard MRL estimator in simple random sampling using Monte Carlo simulation and a real dataset from the Surveillance, Epidemiology, and End Results Program. Our results indicate the superiority of the procedures that we have developed when the quality of ranking is fairly good.
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ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2023.2301334