Estimator Of Nonparametric Component Research Articles

The asymptotic properties for the estimators in a semiparametric regression model based on m-asymptotic negatively associated errors

In this paper, we investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on m-asymptotic negatively associated (m-ANA, for short) random variables. The r-th (r > 1) mean consistency, complete consistency and uniform consistency are obtained under some suitable conditions. In order to assess the finite sample performance, we also present a numerical simulation in the last section of the paper. The results obtained in the paper extend the corresponding ones for independent random errors, ?-mixing and other dependent random errors.

Weak consistency for the estimators in a semiparametric regression model based on negatively associated random errors

In this article, we consider the semiparametric regression model: , which is an important and very useful statistical model. We investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on negatively associated random errors. The weak consistency for the estimators and of and respectively are established under some suitable conditions. In addition, a simulation to study the numerical performance of the consistency for the nearest neighbor weight function estimators is provided and a real data application is then presented.

Strong Consistency of Estimators in a Partially Linear Model with Asymptotically Almost Negatively Associated Errors

This paper studies a heteroscedastic partially linear regression model in which the errors are asymptotically almost negatively associated (AANA, in short) random variables with not necessarily identical distribution and zero mean. Under some mild conditions, we establish the strong consistency of least squares estimators, weighted least squares estimators, and the ultimate weighted least squares estimators for the unknown parameter, respectively. In addition, the strong consistency of the estimator for nonparametric component is also investigated. The results derived in the paper include the corresponding ones of independent random errors and some dependent random errors as special cases. At last, two simulations are carried out to study the numerical performance of the strong consistency for least squares estimators and weighted least squares estimators of the unknown parametric and nonparametric components in the model.

Semi-functional partial linear spatial autoregressive model

This paper proposes a semi-functional partial linear spatial autoregressive (SAR) model, in which we allow one of the explanatory variables to be a functional variable, while the dependent variable is scalar. We aim to enable the spatial econometric models to be applied to various areas where data types can be functional data. Based on quasi-maximum likelihood estimation (QMLE) method and local linear regression method, we construct a two-stage estimator to estimate the parameters and nonparametric component. The convergence rate of the estimator of nonparametric component is given. Furthermore, Monte Carlo simulations are performed to investigate our two-stage estimator’s finite sample performance.

Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters

In this article, we consider variable selection in semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters. With the nonparametric functions approximated by B-spline basis functions and combining 2SLS method with the SCAD penalty, we propose a variable selection procedure. Under mild conditions, we establish the consistency and oracle property of the resulting estimators for parameter components and consistency of the regularized estimator for nonparametric component. Some simulation studies are conducted to assess the finite sample performance of the proposed variable selection procedure, and the developed methodology is illustrated by an analysis of the Boston housing price data.

A note on the asymptotic properties of the estimators in a semiparametric regression model

In this paper, we investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on -mixing random errors. The r-th mean consistency and uniform consistency are established under some suitable conditions. Finally, a simulation to study the numerical performance of the consistency for the nearest neighbor weight function estimators is provided. The results obtained in the paper complement the existing ones of independent random errors to the case of -mixing random errors.

Semiparametric statistical inferences for longitudinal data with nonparametric covariance modelling

ABSTRACTIn this paper, semiparametric modelling for longitudinal data with an unstructured error process is considered. We propose a partially linear additive regression model for longitudinal data in which within-subject variances and covariances of the error process are described by unknown univariate and bivariate functions, respectively. We provide an estimating approach in which polynomial splines are used to approximate the additive nonparametric components and the within-subject variance and covariance functions are estimated nonparametrically. Both the asymptotic normality of the resulting parametric component estimators and optimal convergence rate of the resulting nonparametric component estimators are established. In addition, we develop a variable selection procedure to identify significant parametric and nonparametric components simultaneously. We show that the proposed SCAD penalty-based estimators of non-zero components have an oracle property. Some simulation studies are conducted to examine the finite-sample performance of the proposed estimation and variable selection procedures. A real data set is also analysed to demonstrate the usefulness of the proposed method.

The asymptotic properties of the estimators in a semiparametric regression model

In this paper, we investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on $$\varphi $$ -mixing random variables. The rth mean consistency, complete consistency, uniform rth mean consistency and uniform complete consistency are established under some suitable conditions. In addition, a simulation to study the numerical performance of the consistency of the nearest neighbor weight function estimators is provided. The results obtained in the paper improve the conditions in the literature and generalize the existing results of independent random errors to the case of $$\varphi $$ -mixing random errors.

Asymptotic normality of DHD estimators in a partially linear model

The paper studies a partially linear regression model given by $$\begin{aligned} y_i=x_i^T\beta +f(t_i)+\varepsilon _i,i=1,2,\ldots ,n, \end{aligned}$$ where \(\{\varepsilon _i,i=1,2,\ldots , n\}\) are independent and identically distributed random errors with zero mean and finite variance \(\sigma ^2>0\). Using a difference based and the Huber–Dutter (DHD) approaches, the estimators of unknown parametric component \(\beta \) and root variance \(\sigma \) are given, and then the estimation of nonparametric component \(f(\cdot )\) is given by the wavelet method. The asymptotic normality of the DHD estimators of \(\beta \) and \(\sigma \) are investigated, and the weak convergence rate of the estimator of \(f(\cdot )\) is also investigated. In addition, for stationary \(m\)-dependent sequence of random variables, the central limit theorem is also obtained. At last, two examples are presented to illustrate the proposed method.

Finite Mixture of Generalized Semiparametric Models: Variable Selection via Penalized Estimation

Selection of the important variables is one of the most important model selection problems in statistical applications. In this article, we address variable selection in finite mixture of generalized semiparametric models. To overcome computational burden, we introduce a class of variable selection procedures for finite mixture of generalized semiparametric models using penalized approach for variable selection. Estimation of nonparametric component will be done via multivariate kernel regression. It is shown that the new method is consistent for variable selection and the performance of proposed method will be assessed via simulation.

Efficient statistical inference for partially nonlinear errors-in-variables models

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 sample performances of the proposed statistical inference procedures are illustrated by Monte Carlo simulation studies.

Empirical likelihood for single-index varying-coefficient models

In this paper, we develop statistical inference techniques for the unknown coefficient functions and single-index parameters in single-index varying-coefficient models. We first estimate the nonparametric component via the local linear fitting, then construct an estimated empirical likelihood ratio function and hence obtain a maximum empirical likelihood estimator for the parametric component. Our estimator for parametric component is asymptotically efficient, and the estimator of nonparametric component has an optimal convergence rate. Our results provide ways to construct the confidence region for the involved unknown parameter. We also develop an adjusted empirical likelihood ratio for constructing the confidence regions of parameters of interest. A simulation study is conducted to evaluate the finite sample behaviors of the proposed methods.

Asymptotic properties of wavelet estimators in a semiparametric regression model with censored data

Consider a semiparametric regression model Yi = Xiβ + g(ti) + ei, 1 ⩾ i ⩾ n, where Yi is censored on the right by another random variable Ci with known or unknown distribution G. The wavelet estimators of parameter and nonparametric part are given by the wavelet smoothing and the synthetic data methods. Under general conditions, the asymptotic normality for the wavelet estimators and the convergence rates for the wavelet estimators of nonparametric components are investigated. A numerical example is given.

Statistical inference for a single-index varying-coefficient model

We investigate the estimators of parameters of interest for a single-index varying-coefficient model. To estimate the unknown parameter efficiently, we first estimate the nonparametric component using local linear smoothing, then construct an estimator of parametric component by using estimating equations. Our estimator for the parametric component is asymptotically efficient, and the estimator of nonparametric component has asymptotic normality and optimal uniform convergence rate. Our results provide ways to construct confidence regions for the involved unknown parameters. The finite-sample behavior of the new estimators is evaluated through simulation studies, and applications to two real data are illustrated.

Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data

We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes estimation and model selection very challenging problems. We propose consistent estimation and model selection that incorporate the correlation structure. We establish an asymptotic property with L2-norm consistency for the nonparametric components, which achieves the optimal rate of convergence. In addition, the proposed model selection strategy is able to select the correct generalized additive model consistently. That is, with probability approaching to 1, the estimators for the zero function components converge to 0 almost surely. We illustrate our method using numerical studies with both continuous and binary responses, along with a real data application of binary periodontal data.Supplemental materials including technical details are available online.

Characterization of the asymptotic distribution of semiparametric M-estimators

This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator.

A two-stage approach to semilinear in-slide models

The semilinear in-slide models (SLIMs) have been shown to be effective methods for normalizing microarray data [J. Fan, P. Tam, G. Vande Woude, Y. Ren, Normalization and analysis of cDNA micro-arrays using within-array replications applied to neuroblastoma cell response to a cytokine, Proceedings of the National Academy of Science (2004) 1135–1140]. Using a backfitting method, [J. Fan, H. Peng, T. Huang, Semilinear high-dimensional model for normalization of microarray data: a theoretical analysis and partial consistency, Journal of American Statistical Association, 471, (2005) 781–798] proposed a profile least squares (PLS) estimation for the parametric and nonparametric components. The general asymptotic properties for their estimator is not developed. In this paper, we consider a new approach, two-stage estimation, which enables us to establish the asymptotic normalities for both of the parametric and nonparametric component estimators. We further propose a plug-in bandwidth selector using the asymptotic normality of the nonparametric component estimator. The proposed method allow for the modeling of the aggregated SLIMs case where we can explicitly show that taking the aggregated information into account can improve both of the parametric and nonparametric component estimator by the proposed two-stage approach. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedures.

FIXED-DESIGN SEMIPARAMETRIC REGRESSION FOR LINEAR TIME SERIES

This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.

SEMIPARAMETRIC REGRESSION MODELS WITH LOCALLY GENERALIZED GAUSSIAN ERROR'S STRUCTURE

This paper proposes parametric component and nonparametric component estimators in a semiparametric regression models based on least squares and weight function's method, their strong consistency and rth mean consistency are obtained under a locally generalized Gaussian error's structure. Finally, the author showes that the usual weight functions based on nearest neighbor method satisfy the designed assumptions imposed.