Metrika

, Volume 61, Issue 3, pp 291–308

Estimation of P[Y < X] for generalized exponential distribution

  • Debasis Kundu
  • Rameshwar D. Gupta
Article

DOI: 10.1007/s001840400345

Cite this article as:
Kundu, D. & Gupta, R. Metrika (2005) 61: 291. doi:10.1007/s001840400345

Abstract

This paper deals with the estimation of P[Y < X] when X and Y are two independent generalized exponential distributions with different shape parameters but having the same scale parameters. The maximum likelihood estimator and its asymptotic distribution is obtained. The asymptotic distribution is used to construct an asymptotic confidence interval of P[Y < X]. Assuming that the common scale parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are obtained. Different confidence intervals are proposed. Monte Carlo simulations are performed to compare the different proposed methods. Analysis of a simulated data set has also been presented for illustrative purposes.

Keywords

Stress-strength model maximum likelihood estimator Bayes estimator bootstrap confidence intervals Credible intervals asymptotic distributions 

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Debasis Kundu
    • 1
  • Rameshwar D. Gupta
    • 2
  1. 1.Department of MathematicsIndian Institute of Technology KanpurIndia
  2. 2.Department of Computer Science and Applied StatisticsThe University of New BrunswickSaint JohnCanada

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