Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion

Authors: Martins-Filho, Carlos¹; Yang, Ke²

Source: Journal of Nonparametric Statistics, Volume 19, Number 1, January 2007 , pp. 23-62(40)

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Abstract:

In this paper, we investigate the finite sample performance of four kernel-based estimators that are currently available for additive non-parametric regression models - the classic backfitting estimator (CBE), the smooth backfitting estimator, the marginal integration estimator, and two versions of a two-stage estimator of which the first is proposed by Kim, Linton and Hengartner (1999) and the second is proposed in this paper. The bandwidths are selected for each estimator by minimizing their respective asymptotic approximation of the mean average squared errors. In our simulations, we are particularly concerned with the performance of these estimators under this unified data-driven bandwidth selection method, since in this case both the asymptotic and the finite sample properties of all estimators are currently unavailable. The comparison is based on the estimators' average squared error. Our Monte Carlo results seem to suggest that the CBE is the best performing kernel-based procedure.

Keywords: Additive non-parametric regression; Local linear estimation; Backfitting estimation; Smooth backfitting; Marginal integration

Document Type: Research article

DOI: http://dx.doi.org/10.1080/10485250701297933

Affiliations: 1: Department of Economics, Oregon State University, Corvallis, OR, USA 2: College of Business, Alfred University, Alfred, NY, USA

Publication date: 2007-01-01