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

A. Wiranto, A. Kurniawan, D. A. Fitria, Suliyanto, N. Chamidah

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

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 θ will be better and the result confidence interval ranges narrower.

Original languageEnglish
Article number012060
JournalJournal of Physics: Conference Series
Volume1397
Issue number1
DOIs
Publication statusPublished - 19 Dec 2019
Event6th International Conference on Research, Implementation, and Education of Mathematics and Science, ICRIEMS 2019 - Yogyakarta, Indonesia
Duration: 12 Jul 201913 Jul 2019

Fingerprint

Dive into the research topics of 'Estimation of type i censored exponential distribution parameters using objective bayesian and bootstrap methods (case study of chronic kidney failure patients)'. Together they form a unique fingerprint.

Cite this