Open AccessExponential Distribution Parameter Estimation with Bayesian SELF Method in Survival Analysis
Author(s) -
Yuli Triana,
Joko Purwadi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1373/1/012050
Subject(s) - algorithm , bayesian probability , artificial intelligence , mathematics , bayes estimator , computer science , statistics
This Paper discussed the Exponensial distribution parameter estimation using Bayesian SELF method in survival analysis with θ ^ as SELF Bayesian estimator for the θparameter. Survival analysis corresponds to a method that relates to time starting from the start point to the occurrence of a particular event or an endpoint. The event referred in this research is the event of death. The distribution used in this research is an exponential distribution. Bayesian SELF estimation of θ ^ on exponential distribution for the censored data was obtained by minimizing expectations of loss function. Application of Bayesian SELF method on acute coronary syndrome patient’s data, it was obtained θ ^ = 0.18583 that indicates the patients’ probability to survive is high and the probability for the patients to fail is low.