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 whol...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.展开更多
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 wid...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.展开更多
基金the support of the National Natural Science Foundation of China(Grant Nos.21025416,20974040,50930001)China Postdoctoral Science Foundation(Grant No.20110491295).
文摘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.
基金This research was partially supported by National Science Foundation of China (NSFC) grants 11571050, 11571051 and 11671054The Education Department of Jilin Province,“13th Five-Year” project planning 2016316.
文摘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.
