Estimation of type I censored exponential distribution parameters using objective bayesian and bootstrap methods (case study of chronic kidney failure patients)

Abstract

Bayesian point estimation is an estimation method based on prior selection and loss function. In Objective Bayesian estimation are chosen prior to Jeffrey and used intrinsic discrepancy loss functions based on the Kullback-Leibler divergence equation which will have a minimum effect of data on the posterior distribution. The objective Bayesian point estimator provides estimates of population parameters based solely on the assumed population distribution and data. The goal of this paper is to estimate parameters from the exponential distribution on type II censored data using the objective Bayesian and bootstrap methods. The bootstrap method is used to resampling and built a confidence intervals for parameters whhich will be estimated. The methods were applied on the life-time data of 63 patients of chronic renal failure and the initial diagnosis was non-diabetic disease with bootstrap methods using 10, 100, and 1000 times used in this study. So that the bigger bootstrap samples rendered the estimated value θ̂ will be better and the result confidence interval ranges narrower.