Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95821
Title: Variable selection for high-dimensional generalized varying-coefficient models
Authors: Lian, Heng
Keywords: DRNTU::Science::Chemistry
Issue Date: 2012
Source: Lian, H. (2012). Variable selection for high-dimensional generalized varying-coefficient models. Statistica Sinica, 22, 1563-1588.
Series/Report no.: Statistica sinica
Abstract: In this paper, we consider the problem of variable selection for high-dimensional generalized varying-coefficient models and propose a polynomial-spline based procedure that simultaneously eliminates irrelevant predictors and estimates the nonzero coefficients. In a ``large , small " setting, we demonstrate the convergence rates of the estimator under suitable regularity assumptions. In particular, we show the adaptive group lasso estimator can correctly select important variables with probability approaching one and the convergence rates for the nonzero coefficients are the same as the oracle estimator (the estimator when the important variables are known before carrying out statistical analysis). To automatically choose the regularization parameters, we use the extended Bayesian information criterion (eBIC) that effectively controls the number of false positives. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed procedures.
URI: https://hdl.handle.net/10356/95821
http://hdl.handle.net/10220/11777
ISSN: 1017-0405
DOI: http://dx.doi.org/10.5705/ss.2010.308
Rights: © 2012 Academia Sinica, Institute of Statistical Science.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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