Please use this identifier to cite or link to this item: https://doi.org/10.5705/ss.2010.105
Title: Adaptive semi-varying coefficient model selection
Authors: Hu, T.
Xia, Y. 
Keywords: BIC
Kernel smoothing
LASSO
Model selection
Oracle property
SCAD
Semi-varying coefficient model
Issue Date: Apr-2012
Citation: Hu, T., Xia, Y. (2012-04). Adaptive semi-varying coefficient model selection. Statistica Sinica 22 (2) : 575-599. ScholarBank@NUS Repository. https://doi.org/10.5705/ss.2010.105
Abstract: Identification of constant coefficients in a semi-varying coefficient model is an important issue (Zhang et al (2002)). We propose a novel method for this by combining local polynomial smoothing (Fan and Zhang (1999)) with shrinkage estimation (Tibshirani (1996)). Unlike the stepwise procedure (Xia, Zhang, and Tong (2004)), our method can identify the constant coefficients and estimate the model simultaneously. By imposing the adaptive LASSO penalty and starting with the Nadaraya-Watson estimator, the method can identify the constant coefficients and varying coefficients consistently, and estimate the model with oracle efficiency (Fan and Li (2001)).
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/104986
ISSN: 10170405
DOI: 10.5705/ss.2010.105
Appears in Collections:Staff Publications

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