Linear regression models for interval-valued data have been widely studied.Most literatures are to split an interval into two real numbers,i.e.,the left-and right-endpoints or the center and radius of this interval,an...Linear regression models for interval-valued data have been widely studied.Most literatures are to split an interval into two real numbers,i.e.,the left-and right-endpoints or the center and radius of this interval,and fit two separate real-valued or two dimension linear regression models.This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix,based on a generalized linear model for interval-valued data.A three step estimation method is proposed.Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.展开更多
In this paper,we study the quasi-stationarity and quasi-ergodicity of general Markov processes.We show,among other things,that if X is a standard Markov process admitting a dual with respect to a finite measure m and ...In this paper,we study the quasi-stationarity and quasi-ergodicity of general Markov processes.We show,among other things,that if X is a standard Markov process admitting a dual with respect to a finite measure m and if X admits a strictly positive continuous transition density p(t,x,y)(with respect to m)which is bounded in(x,y)for every t>0,then X has a unique quasi-stationary distribution and a unique quasi-ergodic distribution.We also present several classes of Markov processes satisfying the above conditions.展开更多
基金the National Nature Science Foundation of China under Grant Nos.11571024and 11771032the Humanities and Social Science Foundation of Ministry of Education of China under Grant No.20YJCZH245。
文摘Linear regression models for interval-valued data have been widely studied.Most literatures are to split an interval into two real numbers,i.e.,the left-and right-endpoints or the center and radius of this interval,and fit two separate real-valued or two dimension linear regression models.This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix,based on a generalized linear model for interval-valued data.A three step estimation method is proposed.Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.
基金supported by National Natural Science Foundation of China(GrantNo.11171010)Beijing Natural Science Foundation(Grant No.1112001)
文摘In this paper,we study the quasi-stationarity and quasi-ergodicity of general Markov processes.We show,among other things,that if X is a standard Markov process admitting a dual with respect to a finite measure m and if X admits a strictly positive continuous transition density p(t,x,y)(with respect to m)which is bounded in(x,y)for every t>0,then X has a unique quasi-stationary distribution and a unique quasi-ergodic distribution.We also present several classes of Markov processes satisfying the above conditions.
