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Title: On a principal varying coefficient model
Authors: Jiang, Q.
Wang, H.
Xia, Y. 
Jiang, G.
Keywords: L1 penalty
Local linear estimator
Principal function
Profile least-squares estimation
Semivarying coefficient model
Issue Date: 2013
Citation: Jiang, Q., Wang, H., Xia, Y., Jiang, G. (2013). On a principal varying coefficient model. Journal of the American Statistical Association 108 (501) : 228-236. ScholarBank@NUS Repository.
Abstract: We propose a novel varying coefficient model (VCM), called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional VCM, PVCM reduces the actual number of nonparametric functions and thus has better estimation efficiency. Compared with the semivarying coefficient model (SVCM), PVCM is more flexible but with the same estimation efficiency when the number of principal functions in PVCM and the number of varying coefficients in SVCM are the same. Model estimation and identification are investigated, and the better estimation efficiency is justified theoretically. Incorporating the estimation with the L1 penalty, variables in the linear combinations can be selected automatically, and hence, the estimation efficiency can be further improved. Numerical experiments suggest that the model together with the estimation method is useful even when the number of covariates is large. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Source Title: Journal of the American Statistical Association
ISSN: 01621459
DOI: 10.1080/01621459.2012.736904
Appears in Collections:Staff Publications

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