Bayesian empirical likelihood and variable selection for censored linear model with applications to acute myelogenous leukemia data

This paper develops the Bayesian empirical likelihood (BEL) method and the BEL vari-able selection for linear regression models with censored data.Empirical likelihood is a multivariate analysis tool that has been widely applietl to many fieltls such as biomed-ical and social sciences.By introducing two special priors to the empirical likelihood function,we find two obvious superiorities of the BEL methods,that is (i) more precise coverage probabilities of the BEL credible region and (ii) higher accuracy and correct identification rate of the BEL model selection using an hierarchical Bayesian models vs.some current methods such as the LASSO,A LASSO and SCAD.The numerical simu-lations and empirical analysis of two data examples show strong competitiveness of the proposed method.

A study of chemical reactions in coarse-grained simulations

We introduce a reaction model for use in coarse-grained simulations to study the chemical reactions in polymer systems at mesoscopic level.In this model,we employ an idea of reaction probability in control of the whole process of chemical reactions.This model has been successfully applied to the studies of surface initiated polymerization process and the network structure formation of typical epoxy resin systems.It can be further modified to study different kinds of chemical reactions at mesoscopic scale.