Robust variable selection for nonlinear models with diverging number of parameters

We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties.

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Year of publication:	2014
Authors:	Lv, Zhike ; Zhu, Huiming ; Yu, Keming
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 91.2014, C, p. 90-97
Publisher:	Elsevier
Subject:	Variable selection \| Asymptotic normality \| Oracle properties \| Nonlinear models \| Modal regression

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

Persistent link: https://www.econbiz.de/10010776529