Convergence in probability of the Mallows and GCV wavelet and Fourier regularization methods

Wavelet and Fourier regularization methods are effective for the nonparametric regression problem. We prove that the loss function evaluated for the regularization parameter chosen through GCV or Mallows criteria is asymptotically equivalent in probability to its minimum over the regularization parameter.

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Year of publication:	2001
Authors:	Amato, Umberto ; De Canditiis, Daniela
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 54.2001, 3, p. 325-329
Publisher:	Elsevier
Keywords:	Nonparametric regression Wavelet series Fourier series GCV Mallows criterion Convergence

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005223831