supported by the Fundamental Research Funds for the Central University (Grant No.19JNLH09);Innovation Team Project in Guangdong Province,P.R.China (Grant No.2016WCXTD004);supported by the National Natural Science Foundation of China (Grants no.11731012,12271062);Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering (Changsha University of Science&Technology)。
Let(Z_(n))be a supercritical bisexual branching process in a random environmentξ.We study the almost sure(a.s.)convergence rate of the submartingale W_(n)=Z_(n)/In to its limit W,where(In)is an usually used norming s...
Supported by the National Natural Science Foundation of China(No.11801567)
In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival ...
supported by MOE(Ministry of Education in China),Project of Humanities and Social Sciences(No.15YJA910004);Sponsored by K.C.Wong Magna Fund in Ningbo University;supported by the National Social Science Foundation of China(No.17BTJ025);the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Education(No.KLATASDS1802)
The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data models.In this paper,we proposed a flexible way to study this dependence by using nonparametric r...
In this article, we consider the varying coefficient multiplicative regression model, which is very useful to model the positive response. The criterion of least product relative error(LPRE) is extended to the varying...
Supported by National Social Science Foundation of China(16BTJ015)
In this paper, a varying coefficient errors-in-variables model under longitudinal data is investigated.An empirical likelihood based bias-correction approach is proposed. It is proved that the proposed statistics are ...
supported in part by National Natural Science Foundation of China(11171112,11201190);Doctoral Fund of Ministry of Education of China(20130076110004);Program of Shanghai Subject Chief Scientist(14XD1401600);the 111 Project of China(B14019)
The varying-coefficient partially linear regression model is proposed by combining nonparametric and varying-coefficient regression procedures. Wong, et al. (2008) proposed the model and gave its estimation by the l...
Xu’s research was supported by Key Academic Project from Bureau of Statistics of Zhejiang Province(201325);Research Project of the National Statistics(2013LY119);Bai’s work was partially supported by National Natural Science Funds for Young Scholar(No.11001162);Shanghai University of Finance and Economics through Project 211 Phase IV and Shanghai Leading Academic Discipline Project(No.B804)
In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporane...
supported by National Natural Science Funds for Distinguished Young Scholar(No.70825004) and (No.71271128);Creative Research Groups of China(No.71271128);NCMIS and Shanghai University of Finance and Economics through Project 211 Phase Ⅲ;Shanghai Leading Academic Discipline Project(No.B803)
This paper is concerned with the estimating problem of a semiparametric varying-coefficient partially linear errors-in-variables model Yi=Xτiβ+Zτiα(Ui)+εi , Wi=Xi+ξi,i=1, · · · , n. Due to measurement er...
Supported by the National Natural Science Foundation of China (No.10671139)
The paper gives estimates for the finite-time ruin probability with insurance and financial risks. When the distribution of the insurance risk belongs to the class L(γ) for some γ〉0 or the subexponential distribu...
Supported by the National Natural Science Foundation of China (No. 10871177);Specialized Research Fund for the Doctoral Program of Higher Education (No. 20090101110020)
Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components: model selection for non-parametric components and...