Efficient Estimation in Heteroscedastic Varying Coefficient Models

Efficient Estimation in Heteroscedastic Varying Coefficient Models

This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method.

Efficient estimation and inferences for varying-coefficient models

Efficient estimation of partially linear varying coefficient models

In this paper, we consider the problem of estimating a semiparametric partially linear varying coefficient model. We derive the semiparametric efficiency bound for the asymptotic variance of the finitedimensional parameter estimator. We also propose an efficient estimator for estimating the finitedimensional parameter of the model. Simulation results show substantial efficiency gain of our prop...

Statistical Inference for Heteroscedastic Semi-Varying Coefficient EV Models.dvi

This paper proposes an estimation procedure for a class of semi-varying coefficient regression models when the covariates of the linear part are subject to measurement errors. Initial estimates for the regression and varying coefficients are first constructed by the profile least squares procedure without input from heteroscedasticity, a bias-corrected kernel estimate for the variance function ...

Penalized estimation in additive varying coefficient models using grouped regularization

Additive varying coefficient models are a natural extension of multiple linear regression models, allowing the regression coefficients to be functions of other variables. Therefore these models are more flexible to model more complex dependencies in data structures. In this paper we consider the problem of selecting in an automatic way the significant variables among a large set of variables, w...