Reliability estimation in multicomponent stress–strength model for Topp-Leone distribution
- 17 July 2019
- journal article
- research article
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 89 (15) , 2914-2929
Abstract
In this paper, we consider the estimation reliability in multicomponent stress-strength (MSS) model when both the stress and strengths are drawn from Topp-Leone (TL) distribution. The maximum likelihood (ML) and Bayesian methods are used in the estimation procedure. Bayesian estimates are obtained by using Lindley’s approximation and Gibbs sampling methods, since they cannot be obtained in explicit form in the context of TL. The asymptotic confidence intervals are constructed based on the ML estimators. The Bayesian credible intervals are also constructed using Gibbs sampling. The reliability estimates are compared via an extensive Monte-Carlo simulation study. Finally, a real data set is analysed for illustrative purposes.Keywords
This publication has 35 references indexed in Scilit:
- Estimation of the Reliability of a Stress-Strength System from Power Lindley DistributionsCommunications in Statistics - Simulation and Computation, 2014
- Estimation of Reliability in Multicomponent Stress-Strength based On Inverse Rayleigh DistributionJournal of Statistics Applications & Probability, 2013
- Reliability estimation in stress–strength models: an MCMC approachStatistics, 2013
- Estimation ofP(X>Y) with Topp–Leone distributionJournal of Statistical Computation and Simulation, 2013
- Moments of order statistics of Topp–Leone distributionStatistical Papers, 2010
- On some reliability measures and their stochastic orderings for the Topp–Leone distributionJournal of Applied Statistics, 2005
- Probabilistic production costingInternational Journal of Electrical Power & Energy Systems, 1983
- Approximate Bayesian methodsTrabajos de estadistica y de investigacion operativa, 1980
- Estimation of Reliability in a Multicomponent Stress-Strength ModelJournal of the American Statistical Association, 1974
- A Family of J-Shaped Frequency FunctionsJournal of the American Statistical Association, 1955