In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing respo...In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing response at random. The proposed procedure simultaneously selects significant variables in parametric components and nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.展开更多
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...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 local linear method. In this paper its inference is addressed. Based on these estimates, the generalized like- lihood ratio test is established. Under the null hypotheses the normalized test statistic follows a x2-distribution asymptotically, with the scale constant and the degrees of freedom being independent of the nuisance param- eters. This is the Wilks phenomenon. Furthermore its asymptotic power is also derived, which achieves the optimal rate of convergence for nonparametric hypotheses testing. A simulation and a real example are used to evaluate the performances of the testing procedures empirically.展开更多
探究了在响应变量随机缺失情形下部分线性变系数模型的模型选择和模型平均问题.基于借补方法和Profile最小二乘技术,建立了局部误设定框架下该模型的FIC准则(focused information criterion)和FMA(frequentist model average)估计量,并...探究了在响应变量随机缺失情形下部分线性变系数模型的模型选择和模型平均问题.基于借补方法和Profile最小二乘技术,建立了局部误设定框架下该模型的FIC准则(focused information criterion)和FMA(frequentist model average)估计量,并探究了FIC和FMA的理论性质.模拟研究表明了所提出方法的优越性.最后将提出的方法应用于CD4数据.展开更多
基金Supported by National Natural Science Foundation of China （Grant No. 10871013）, Natural Science Foundation of Beijing （Grant No. 1072004）, and Natural Science Foundation of Guangxi Province （Grant No. 2010GXNSFB013051）
文摘In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing response at random. The proposed procedure simultaneously selects significant variables in parametric components and nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.
基金supported in part by National Natural Science Foundation of China(11171112,11201190)Doctoral Fund of Ministry of Education of China(20130076110004)+1 种基金Program of Shanghai Subject Chief Scientist(14XD1401600)the 111 Project of China(B14019)
文摘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 local linear method. In this paper its inference is addressed. Based on these estimates, the generalized like- lihood ratio test is established. Under the null hypotheses the normalized test statistic follows a x2-distribution asymptotically, with the scale constant and the degrees of freedom being independent of the nuisance param- eters. This is the Wilks phenomenon. Furthermore its asymptotic power is also derived, which achieves the optimal rate of convergence for nonparametric hypotheses testing. A simulation and a real example are used to evaluate the performances of the testing procedures empirically.
文摘探究了在响应变量随机缺失情形下部分线性变系数模型的模型选择和模型平均问题.基于借补方法和Profile最小二乘技术,建立了局部误设定框架下该模型的FIC准则(focused information criterion)和FMA(frequentist model average)估计量,并探究了FIC和FMA的理论性质.模拟研究表明了所提出方法的优越性.最后将提出的方法应用于CD4数据.
