Please use this identifier to cite or link to this item: https://doi.org/10.1198/jasa.2009.0138
Title: Shrinkage estimation of the varying coefficient model
Authors: Wang, H.
Xia, Y. 
Keywords: Bayesian information criterion
Kernel smoothing
Least Absolute Shrinkage and Selection Operator
Oracle property
Smoothly Clipped Absolute Deviation
Variable selection
Varying coefficient model
Issue Date: Jun-2009
Citation: Wang, H., Xia, Y. (2009-06). Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 104 (486) : 747-757. ScholarBank@NUS Repository. https://doi.org/10.1198/jasa.2009.0138
Abstract: The varying coefficient model is a useful extension of the linear regression model. Nevertheless, how to conduct variable selection for the varying coefficient model in a computationally efficient manner is poorly understood. To solve the problem, we propose here a novel method, which combines the ideas of the local polynomial smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO). The new method can do nonparametric estimation and variable selection simultaneously. With a local constant estimator and the adaptive LASSO penalty, the new method can identify the true model consistently, and that the resulting estimator can be as efficient as the oracle estimator. Numerical studies clearly confirm our theories. Extension to other shrinkage methods (e.g., the SCAD, i.e., the Smoothly Clipped Absolute Deviation.) and other smoothing methods is straightforward. © 2009 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105362
ISSN: 01621459
DOI: 10.1198/jasa.2009.0138
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