Principal varying coefficient estimator for high-dimensional models. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Zhao, Weihua; Zhang, Fode; Wang, Xuejun; Li, Rui; Lian, Heng
• Is Part Of: Statistics. Volume 53:Issue 6(2019)
• Page Start: 1234
• Page End: 1250
• Publication Date: 2019-11-02
• Subjects: Asymptotic properties -- B-splines -- sub-Gaussian distribution -- ultra-high dimensionality; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2019.1663521
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: We consider principal varying coefficient models in the high-dimensional setting, combined with variable selection, to reduce the effective number of parameters in semiparametric modelling. The estimation is based on B-splines approach. For the unpenalized estimator, we establish non-asymptotic bounds of the estimator and then establish the (asymptotic) local oracle property of the penalized estimator, as well as non-asymptotic error bounds. Monte Carlo studies reveal the favourable performance of the estimator and an application on a real dataset is presented.