@Article{cmc.2020.10944,
AUTHOR = {Amer Ibrahim Al-Omari, Ibrahim M. Almanjahie, Amal S. Hassan, Heba F. Nagy},
TITLE = {Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {835--857},
URL = {http://www.techscience.com/cmc/v64n2/39332},
ISSN = {1546-2226},
ABSTRACT = {In reliability analysis, the stress-strength model is often used to describe the life of 
a component which has a random strength (<i>X</i>) and is subjected to a random stress (<i>Y</i>). In this 
paper, we considered the problem of estimating the reliability <i>R</i>=<i>P</i> [<i>Y</i><<i>X</i>] when the 
distributions of both stress and strength are independent and follow exponentiated Pareto 
distribution. The maximum likelihood estimator of the stress strength reliability is calculated
under simple random sample, ranked set sampling and median ranked set sampling methods. 
Four different reliability estimators under median ranked set sampling are derived. Two 
estimators are obtained when both strength and stress have an odd or an even set size. The 
two other estimators are obtained when the strength has an odd size and the stress has an 
even set size and vice versa. The performances of the suggested estimators are compared 
with their competitors under simple random sample via a simulation study. The simulation 
study revealed that the stress strength reliability estimates based on ranked set sampling and 
median ranked set sampling are more efficient than their competitors via simple random 
sample. In general, the stress strength reliability estimates based on median ranked set 
sampling are smaller than the corresponding estimates under ranked set sampling and simple 
random sample methods.},
DOI = {10.32604/cmc.2020.10944}
}