Inference for the Chen Distribution Under Progressive First-Failure Censoring
- 24 Downloads
We consider estimation and prediction for the two-parameter Chen distribution on the basis of progressive first failure censoring. The classical estimates of parameters are obtained. In particular, the least square estimation is considered. Further Bayes estimates are derived using the Tierney and Kadane method. We have also used the MH algorithm for this purpose. Further asymptotic, Bonferroni and Bayesian intervals are constructed. The one- and two-sample prediction problems are discussed as well. We examine the performance of studied methods by performing simulations. We analyze a real data set for illustrative purposes, and some concluding remarks are also presented.
KeywordsBayes estimates HPD interval Least square estimation Maximum likelihood estimates Progressive first failure censoring
Authors are thankful to a reviewer for very helpful comments which led to significant improvement in both content and presentation of the manuscript. Authors also thank the Editor for constructive suggestions. Yogesh Mani Tripathi gratefully acknowledges the partial financial support for this research work under a Grant EMR/2016/001401 SERB, India.
- 1.Balakrishnan N, Aggarwala R (2000) Progressive censoring: theory, methods, and applications. Springer, BerlinGoogle Scholar
- 22.Swain JJ, Venkatraman S, Wilson JR (1988) Least-squares estimation of distribution functions in Johnson’s translation system. J Stat Comput Simul 29(4):271–297Google Scholar
- 24.Helu A (2015) On the maximum likelihood and least squares estimation for the inverse Weibull parameters with progressively first-failure censoring. Open J Stat 5(01):75Google Scholar
- 26.Kyriakides E, Heydt GT (2006) Calculating confidence intervals in parameter estimation: a case study. IEEE Trans Power Deliv 21(1):508–509Google Scholar
- 30.Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092Google Scholar