Estimation of using Modifications of Ranked Set Sampling for Weibull Distribution

Fatma Gul Akgul, Birdal Senoglu

Abstract


In statistical literature, estimation of R=P(X<Y) is a commonly-investigated problem, and consequently, there have been considerable number of studies dealing with its estimation of it under simple random sampling (SRS). However, in recent years, the ranked set sampling (RSS) method have been widely-used in the estimation of R. In this study, we consider the estimation of R when the distribution of the both stress and strength are Weibull under the modification of RSS, which are extreme ranked set sampling (ERSS), median ranked set sampling (MRSS) and percentile ranked set sampling (PRSS). We obtain the estimators of R using the maximum likelihood (ML) and the modified maximum likelihood (MML) methodologies under these modifications. Then the performances of proposed estimators are compared with the corresponding ML and MML estimators of R using SRS via a Monte-Carlo simulation study.


Keywords


Stress-strength model, extreme ranked set sampling, median ranked set sampling, percentile ranked set sampling, efficiency.

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DOI: http://dx.doi.org/10.18187/pjsor.v13i4.2056

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Title

Estimation of using Modifications of Ranked Set Sampling for Weibull Distribution

Keywords

Stress-strength model, extreme ranked set sampling, median ranked set sampling, percentile ranked set sampling, efficiency.

Description

In statistical literature, estimation of R=P(X<Y) is a commonly-investigated problem, and consequently, there have been considerable number of studies dealing with its estimation of it under simple random sampling (SRS). However, in recent years, the ranked set sampling (RSS) method have been widely-used in the estimation of R. In this study, we consider the estimation of R when the distribution of the both stress and strength are Weibull under the modification of RSS, which are extreme ranked set sampling (ERSS), median ranked set sampling (MRSS) and percentile ranked set sampling (PRSS). We obtain the estimators of R using the maximum likelihood (ML) and the modified maximum likelihood (MML) methodologies under these modifications. Then the performances of proposed estimators are compared with the corresponding ML and MML estimators of R using SRS via a Monte-Carlo simulation study.


Date

2017-12-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 4, 2017



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810