Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Estimation of Stress-Strength Reliability for the Generalized Pareto Distribution Based on Progressively Censored Samples

Estimation of Stress-Strength Reliability for the Generalized Pareto Distribution Based on... This paper deals with the estimation of stress-strength reliability parameter, $$R = P\left( Y < X \right) $$ R = P Y < X , based on progressively type II censored samples when stress, strength are two independent generalized Pareto random variables. The maximum likelihood estimators, their asymptotic distributions, asymptotic confidence intervals, bootstrap based confidence intervals and Bayes estimators are derived for $$R$$ R . Using Monte Carlo simulations, the MSE, Bayes risk estimators, credible sets and coverage probabilities are computed and compared. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Estimation of Stress-Strength Reliability for the Generalized Pareto Distribution Based on Progressively Censored Samples

Rezaei, S.; Noughabi, R.; Nadarajah, S.
Annals of Data Science , Volume 2 (1) – Apr 3, 2015
Download PDF
19 pages

Loading next page...
 
/lp/springer-journals/estimation-of-stress-strength-reliability-for-the-generalized-pareto-uUK90Yw4DA
Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Economics / Management Science; Business/Management Science, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-015-0033-0
Publisher site
See Article on Publisher Site

Abstract

This paper deals with the estimation of stress-strength reliability parameter, $$R = P\left( Y < X \right) $$ R = P Y < X , based on progressively type II censored samples when stress, strength are two independent generalized Pareto random variables. The maximum likelihood estimators, their asymptotic distributions, asymptotic confidence intervals, bootstrap based confidence intervals and Bayes estimators are derived for $$R$$ R . Using Monte Carlo simulations, the MSE, Bayes risk estimators, credible sets and coverage probabilities are computed and compared.

Journal

Annals of Data Science – Springer Journals

Published: Apr 3, 2015

References

  • Inference of $$R=P[X
    Babayi, S; Khorrama, E; Tondro, F
  • Progressive censoring methodology: an appraisal (with discussions)
    Balakrishnan, N
  • Progressive censoring: theory, methods, and applications
    Balakrishnan, N; Aggarwala, R
  • Shrinkage estimation of $$P(X
    Baklizi, A; El-Masri, AQ
  • On statistics independent of a complete sufficient statistic
    Basu, D
  • Statistical decision theory and bayesian analysis
    Berger, JO
  • Bayesian inference in statistical analysis
    Box, GE; Tiao, GC
  • A simple algorithm for generating random variates with a logconcave density function
    Devroye, L
  • On distributions of generalized order statistics
    Kamps, U; Cramer, E
  • The stress-strength model and its generalizations: theory and applications
    Kotz, S; Lumelskii, Y; Pensky, M
  • Inference on reliability in two-parameter exponential stressstrength model
    Krishnamoorthy, K; Mukherjee, S; Guo, H
  • Approximation bayesian methods
    Lindley, DV
  • Reliability of a stressstrength model with Burr type III distributions
    Mokhlis, NA
  • Reliability for life time distributions
    Nadarajah, S
  • Reliability for logistic distributions
    Nadarajah, S
  • Reliability for Laplace distributions
    Nadarajah, S
  • Reliability for some bivariate beta distributions
    Nadarajah, S
  • Reliability for some bivariate gamma distributions
    Nadarajah, S
  • Reliability for some bivariate exponential distributions
    Nadarajah, S; Kotz, S
  • Maximum likelihood estimation and confidence intervals of system reliability for Gompertz distributions stressstrength models
    Saraoglu, B; Kaya, MF
  • Linear order statistic estimation for the two-parameter Weibull and extreme value distribution from type II progressively censored samples
    Thomas, DR; Wilson, WM