Efficient estimation for the heteroscedastic single-index 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 model selection for single-index varying-coefficient models

The single-index varying-coefficient models include many types of popular semiparametric models, i.e. single-index models, partially linear models, varying-coefficient models, and so on. In this paper, we first establish the semiparametric efficiency bound for the single-index varying-coefficient model, and develop an estimation method based on the efficient estimating equations. Although our m...

Varying Index Coefficient Models

It has been a long history of using interactions in regression analysis to investigate alterations in covariate-effects on response variables. In this article, we aim to address two kinds of new challenges arising from the inclusion of such high-order effects in the regression model for complex data. The first kind concerns a situation where interaction effects of individual covariates are weak...

Empirical likelihood for single-index varying-coefficient models

In this paper, we develop statistical inference techniques for the unknown coefficient functions and singleindex 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. O...

Efficient estimation and inferences for varying-coefficient models