Version 1
: Received: 13 June 2023 / Approved: 13 June 2023 / Online: 13 June 2023 (15:53:52 CEST)
How to cite:
Akdam, N. Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine. Preprints2023, 2023060954. https://doi.org/10.20944/preprints202306.0954.v1
Akdam, N. Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine. Preprints 2023, 2023060954. https://doi.org/10.20944/preprints202306.0954.v1
Akdam, N. Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine. Preprints2023, 2023060954. https://doi.org/10.20944/preprints202306.0954.v1
APA Style
Akdam, N. (2023). Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine. Preprints. https://doi.org/10.20944/preprints202306.0954.v1
Chicago/Turabian Style
Akdam, N. 2023 "Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine" Preprints. https://doi.org/10.20944/preprints202306.0954.v1
Abstract
The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh Weibull (RW) distribution based on progres-sive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley and Tierney-Kadane's approximation method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes esti-mates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. In addition, the coverage probabilities of the parametric bootstrap estimates are calculated. Real lifetime data sets belonging to the cancer types as bladder cancer, head and neck cancer, and leukemia are used to illustrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.
Keywords
Rayleigh Weibull distribution; Progressive type-II censored sample; Bayes estimator; approximate Bayes estimator; Bootstarp intervals; Lindley’s approximation; Tierney-Kadane’s approximation; Squared-error loss function; Monte Carlo simulation
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
