Adaptive orthogonal series estimation in additive stochastic regression models

In this paper, we consider additive stochastic nonparametric regression models. By approximating the nonparametric components by a class of orthogonal series and using a generalized cross-validation criterion, an adaptive and simultaneous estimation procedure for the nonparametric components is constructed. We illustrate the adaptive and simultaneous estimation procedure by a number of simulated and real examples.

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Year of publication:	2006-06-15
Authors:	Wolff, Rodney C ; Gao, Jiti ; Tong, Howell
Institutions:	School of Economics and Finance, Business School
Subject:	Adaptive estimation \| additive model \| dependent process \| mixing condition \| nonlinear time series \| nonparametric regression \| orthogonal series \| strict stationarity \| truncation parameter

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Extent:	application/pdf
Series:	School of Economics and Finance Discussion Papers and Working Papers Series.
Type of publication:	Book / Working Paper
Notes:	Number 208k
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008694534