Sanying Feng

• PhD
• Zhengzhou University

The heterogeneous treatment effect (HTE) is estimated by using the semiparametric regression method. Firstly, a flexible semiparametric single-index model is considered by assuming the nonparametric link function and the interaction between treatment and covariates, and the index parameter vector and the unknown link function are estimated by using...

Functional regression allows for a scalar response to be dependent on a functional predictor, however, not much work has been done when scalar predictors that interacts with the functional predictor is introduced. In this paper, we introduce a new functional single-index varying coefficient model with the functional predictor being single-index par...

This paper considers the estimation for a partial index additive regression model, when the response variable and covariates in the index part are observed with additive distortion measurement errors. For the index parameter, the dimension-reduction based estimators with or without additive distortion measurement errors are proposed. This new estim...

In this paper, we propose a new identifiability condition by using the logarithmic calibration for the multiplicative distortion partial linear measurement errors models, when neither the response variable nor the covariates in the parametric part can be directly observed. We propose a logarithmic calibration estimation procedure for the unobserved...

We introduce a new partially linear functional additive model, and we consider the problem of variable selection for this model. Based on the functional principal components method and the centered spline basis function approximation, a new variable selection procedure is proposed by using the smooth-threshold estimating equation (SEE). The propose...

In this paper, we consider the empirical likelihood inferences of the partial functional linear model with missing responses. Two empirical log-likelihood ratios of the parameters of interest are constructed, and the corresponding maximum empirical likelihood estimators of parameters are derived. Under some regularity conditions, we show that the p...

A partially time-varying coefficient time series panel data model with fixed effects is considered to characterize the nonlinearity and trending phenomenon in panel data model. To estimate the linear regression coefficient and the time-varying coefficient function, two methods are applied with the help of profile least squares. The first one is tak...

In this paper, we propose a nested modified Cholesky decomposition for modeling the covariance structure in multivariate longitudinal data analysis. The entries of this decomposition have simple structures and can be interpreted as the generalized moving average coefficient matrices and innovation covariance matrices. We model the elements of these...

Reduced rank regression is considered when the criterion function is possibly non-smooth, which includes the previously un-studied reduced rank quantile regression. The approach used is based on empirical likelihood with a rank constraint. Asymptotic properties of the maximum empirical likelihood estimator (MELE) are established using general resul...

In this paper, we consider the problem of variable selection and model detection in varying coefficient models with longitudinal data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of...

In this paper, we introduce a new partially functional linear varying coefficient model, where the response is a scalar and some of the covariates are functional. By means of functional principal components analysis and local linear smoothing techniques, we obtain the estimators of coefficient functions of both function-valued variable and real-val...

Two popular variable screening methods under the ultra-high dimensional setting with the desirable sure screening property are the sure independence screening (SIS) and the forward regression (FR). Both are classical variable screening methods and recently have attracted greater attention under the new light of high-dimensional data analysis. We co...

Two popular variable screening methods under the ultra-high dimensional setting with the desirable sure screening property are the sure independence screening (SIS) and the forward regression (FR). Both are classical variable screening methods and recently have attracted greater attention under the new light of high-dimensional data analysis. We co...

Single-index varying coefficient model (SIVCM) is a powerful tool for modelling nonlinearity in multivariate estimation, and has been widely used in the literature due to the fact that it can overcome the well-known phenomenon of “curse-of-dimensionality”. In this paper, we consider the problem of model detection and estimation for SIVCM. Based on...

We consider joint rank and variable selection in multivariate regression. Previously proposed joint rank and variable selection approaches assume that different responses are related to the same set of variables, which suggests using a group penalty on the rows of the coefficient matrix. However, this assumption may not hold in practice and motivat...

This paper considers the estimation of the common probability density of independent and identically distributed variables observed with additive measurement errors. The self-consistent estimator of the density function is constructed when the error distribution is known, and a modification of the self-consistent estimation is proposed when the err...

In this paper, we consider the partially nonlinear errors-in-variables models when the nonparametric component is measured with additive error. The profile nonlinear least squares estimator of unknown parameter and the estimator of nonparametric component are constructed, and their asymptotic properties are derived under general assumptions. Finite...

For the marginal longitudinal generalized linear models (GLMs), we develop the empirical Cressie-Read (ECR) test statistic approach which has been proposed for the independent identically distributed (i.i.d.) case. The ECR test statistic includes empirical likelihood as a special case. By adopting this ECR test statistic approach and taking into ac...

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...

In this paper, we consider the problem of variable selection for partially varying coefficient single-index model, and present a regularized variable selection procedure by combining basis function approximations with smoothly clipped absolute deviation penalty. The proposed procedure simultaneously selects significant variables in the single-index...

We consider the problem of variable selection for single-index varying-coefficient model, and present a regularized variable selection procedure by combining basis function approximations with SCAD penalty. The proposed procedure simultaneously selects significant covariates with functional coefficients and local significant variables with parametr...

We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero,...

We propose an empirical likelihood method for application to a partially linear panel data model with fixed effects. The empirical log-likelihood ratio statistic is proved to be asymptotically chi-squared distributed, and the asymptotic properties of estimators for both the parametric and nonparametric components are established.

The varying coefficient partially linear model is considered in this paper. When the plug-in estimators of coefficient functions are used, the resulting smoothing score function becomes biased due to the slow convergence rate of nonparametric estimations. To reduce the bias of the resulting smoothing score function, a profile-type smoothed score fu...

The empirical likelihood method is especially useful for constructing confidence intervals or regions of parameters of interest. Yet, the technique cannot be directly applied to partially linear single-index models for longitudinal data due to the within-subject correlation. In this paper, a bias-corrected block empirical likelihood (BCBEL) method...

By means of the notion of likelihood ratio, the limit properties of the sequences of arbitrary-dependent continuous random variables are studied, and a kind of strong limit theorems represented by inequalities with random bounds for functions of continuous random variables is established. The Shannon-McMillan theorem is extended to the case of arbi...