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Robust estimation and outlier detection for varying-coefficient models via penalized regression
Communications in Statistics - Simulation and Computation ( IF 0.8 ) Pub Date : 2020-07-02 , DOI: 10.1080/03610918.2020.1784429 Guangren Yang 1 , Sijia Xiang 2 , Weixin Yao 3 , Lin Xu 2
更新日期：2020-07-02
Communications in Statistics - Simulation and Computation ( IF 0.8 ) Pub Date : 2020-07-02 , DOI: 10.1080/03610918.2020.1784429 Guangren Yang 1 , Sijia Xiang 2 , Weixin Yao 3 , Lin Xu 2
Affiliation
Abstract
Varying-coefficient models (VCMs) are widely used in a variety of statistical applications. However, the classical VCMs based on least squares are prone to the presence of even a few severe outliers. In this article, a mean shift parameter is added for each observation to reflect outliers, and different penalties are then applied to the shift parameters to get sparse estimates. The jointly penalized optimization problem is solved through an efficient algorithm, and the tuning parameters are chosen by the Bayesian information criteria (BIC). The efficiency of the new approach is demonstrated via simulation studies as well as a real application on the Hong Kong environmental data.
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