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http://dx.doi.org/10.29220/CSAM.2018.25.4.355

Maximum penalized likelihood estimation for a stress-strength reliability model using complete and incomplete data  

Hassan, Marwa Khalil (Department of Mathematics, Ain Shams University)
Publication Information
Communications for Statistical Applications and Methods / v.25, no.4, 2018 , pp. 355-371 More about this Journal
Abstract
The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.
Keywords
maximum penalized likelihood estimator; Lindley approximation; progressive type II censored data; Bayes estimator; simulation; Markov chain Mote Carlo technique; two parameter negative exponential distribution;
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