Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2012.08.015
Title: Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Authors: Luo, S.
Chen, Z. 
Keywords: Diverging number of parameters
Extended Bayes information criterion
Feature selection
High dimensional feature space
Penalized likelihood
Selection consistency
Issue Date: Mar-2013
Citation: Luo, S., Chen, Z. (2013-03). Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces. Journal of Statistical Planning and Inference 143 (3) : 494-504. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2012.08.015
Abstract: In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this paper, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a SCAD cum EBIC procedure. Simulation studies are conducted to demonstrate the performance of the SCAD cum EBIC procedure in finite sample cases. © 2012 Elsevier B.V.
Source Title: Journal of Statistical Planning and Inference
URI: http://scholarbank.nus.edu.sg/handle/10635/105147
ISSN: 03783758
DOI: 10.1016/j.jspi.2012.08.015
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