Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11976
Title: Robust variable selection for nonlinear models with diverging number of parameters
Authors: Lv, Z
Zhu, H
Yu, K
Keywords: Variable selection;Asymptotic normality;Oracle properties;Nonlinear models;Modal regression
Issue Date: 2014
Publisher: Elsevier
Citation: Statistics & Probability Letters, 91 pp. 90 - 97, (2014)
Abstract: 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.
URI: http://www.sciencedirect.com/science/article/pii/S0167715214001448
http://bura.brunel.ac.uk/handle/2438/11976
DOI: http://dx.doi.org/10.1016/j.spl.2014.04.013
ISSN: C
C
0167-7152
Appears in Collections:Dept of Mathematics Research Papers

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