NONPARAMETRIC And Semiparametric Bayesian Estimators in survival function analysis

Abstract

 Most statistical research generally relies on the study of the behaviour of different phenomena during specific time periods and the use of the results of these studies in the development of appropriate recommendations and decision-making and for the purpose of statistical inference on the parameters of the statistical distribution of life times in  The technical staff of most of the manufacturers in the research units of these companies deals with censored data, the main objective of the study of survival is the need to provide information that is the basis for decision making and must clarify the problem and then the goals and limitations of this study and that  It may have different possibilities to perform the desired function successfully, and the Bayesian inference is a statistical inference method where the theory of biz is used to construct statistical models and the conclusion of statistical inferences about the parameters of the sample or the statistical community and in this research has reviewed the methods Non-parametric and semi-primary control data of type I using (Dirichlet) processes and sampling (Gibbs Sampler) and comparing them with survival capabilities to demonstrate their efficiency using the two statistical indicators the average of the integral error boxes (IMSE) and the average absolute relative error ( MAPE), the simulation method was used to generate data using different sample sizes (n = 15, 30, 50, 100), and through the results the researcher reached the superiority of the Semiparametric Bayesian on the on Non-parametric