Bayesian estimation for the Kumaraswamy-inverse Rayleigh distribution based on progressive first failure censored samples

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    This paper considers Bayesian estimation of parameter and reliability function of Kumaraswamy-inverse Rayleigh distribution under the different loss functions with progressively first failure censored samples. We used squared error , minimum expected, weighted and Linex loss functions for obtaining the Bayes estimators of parameter and reliability function. Finally, Comparisons are made between Bayes estimators under different loss functions using simulation study.

    Keywords: Kumaraswamy-inverse Rayleigh distribution, Progressive first failure censoring, Bayesian estimation, reliability function.


  • References


    1. S. J. Wu and C. Kus, On Estimation Based on Progressive First-Failure-Censored Sampling, Computational Statistics and Data Analysis, Vol. 53, No. 10, pp. 3659-3670, 2009.
    2. S.J. Wu, S. R. Huang, Progressively first-failure censored reliability sampling plans with cost constraint, Computational Statistics & Data Analysis, Vol. 56, Issue 6, pp. 2018-2030, 2012.
    3. N. Balakrishnan, R. A. Sandhu, A simple simulation algorithm for generating progressively type-II censored samples, The American Statistician 49, 229-230, 1995.
    4. V. M. Rao Tummala and P. T. Sathe, Minimum expected loss estimators of reliability and parameters of certain lifetime distributions, IEEE Transactions on Reliability, 27,4, 283-285, 1978.
    5. M. Q. Shahbaz, S. Shahbaz, N. S. Butt, The KumaraswamyInverseWeibull Distribution, Pakistan journal of statistics and operation research, 8(3): 479-489, 2012.
    6. M. A. Hussian and E. A. Amin, Estimation and prediction for the Kumaraswamy-inverse Rayleigh distribution based on records , International Journal of Advanced Statistics and Probability, 2,(1), 21-27, 2014.
    7. A. Zellner, Bayes estimation and prediction using asymmetric loss functions, Journal of the American Statistical Association, 81, 446-451, 1986.

 

View

Download

Article ID: 2564
 
DOI: 10.14419/ijsw.v2i2.2564




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.