JSM 2005 - Toronto

Abstract #302293

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 13
Type: Invited
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #302293
Title: Additive Coefficient Modeling via Polynomial Spline
Author(s): Lan Xue*+ and Lijian Yang
Companies: Michigan State University and Michigan State University
Address: Department of Statistics and Probability, East Lansing, MI, 48823, U.S
Keywords: approximation space ; German real GNP ; knots ; mean square convergence ; Spline approximation ; Wolf's sunspot number
Abstract:

In this paper, a flexible nonparametric regression model is considered in which the predictor depends linearly on covariates whose regression coefficients are additive functions of other covariates. Polynomial spline estimation is used to estimate the unknown coefficient functions, and a model selection method is proposed based on a nonparametric version of the Bayes Information Criterion (BIC). Simulation studies and the applications to three real data examples have demonstrated that besides being highly efficient in terms of computing, the polynomial spline estimators also are more accurate than or at least as good as the existing local polynomial-based estimators. Under geometrically strong mixing, mean square convergence of the polynomial spline estimators is established, which provides theoretical justification for the proposed estimation method.

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