Variable selection in semiparametric regression analysis for longitudinal data

In this paper, we present a variable selection procedure by using basis function approximations and a partial group SCAD penalty for semiparametric varying coefficient partially linear models with longitudinal data. With appropriate selection of the tuning parameters, we establish the oracle property of this procedure. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.     

Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition

In this paper, we consider the statistical inference for the partially liner varying coefficient model with measurement error in the nonparametric part when some prior information about the parametric part is available. The prior information is expressed in the form of exact linear restrictions. Two types of local bias-corrected restricted profile least squares estimators of the parametric component and nonparametric component are conducted, and their asymptotic properties are also studied under some regularity conditions. Moreover, we compare the efficiency of the two kinds of parameter estimators under the criterion of Lo?ner ordering. Finally, we develop a linear hypothesis test for the parametric component. Some simulation studies are conducted to examine the finite sample performance for the proposed method. A real dataset is analyzed for illustration.     

Empirical likelihood inferences for semiparametric instrumental variable models

This paper studies the empirical likelihood inferences for a class of semiparametric instrumental variable models. We focus on the case that some covariates are endogenous variables, and some auxiliary instrumental variables are available. An instrumental variable based empirical likelihood method is proposed, and it is shown that the proposed empirical log-likelihood ratio is asymptotically chi-squared. Then, the confidence intervals for the regression coefficients are constructed. Some simulation studies are undertaken to assess the finite sample performance of the proposed empirical likelihood procedure.     

Empirical likelihood for linear models with missing responses

The purpose of this article is to use an empirical likelihood method to study the construction of confidence intervals and regions for the parameters of interest in linear regression models with missing response data. A class of empirical likelihood ratios for the parameters of interest are defined such that any of our class of ratios is asymptotically chi-squared. Our approach is to directly calibrate the empirical log-likelihood ratio, and does not need multiplication by an adjustment factor for the original ratio. Also, a class of estimators for the parameters of interest is constructed, and the asymptotic distributions of the proposed estimators are obtained. Our results can be used directly to construct confidence intervals and regions for the parameters of interest. A simulation study indicates that the proposed methods are comparable in terms of coverage probabilities and average lengths/areas of confidence intervals/regions. An example of a real data set is used for illustrating our methods.