Model identification and selection for single-index varying-coefficient models

Peng Lai, Fangjian Wang, Tingyu Zhu and Qingzhao Zhang ()
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Peng Lai: Nanjing University of Information Science & Technology
Fangjian Wang: Nanjing University of Information Science & Technology
Tingyu Zhu: Oregon State University
Qingzhao Zhang: Xiamen University

Annals of the Institute of Statistical Mathematics, 2021, vol. 73, issue 3, No 2, 457-480

Abstract: Abstract Single-index varying-coefficient models include many types of popular semiparametric models, i.e., single-index models, partially linear models, varying coefficient models, and so on. In this paper, a two-stage efficient variable selection procedure is proposed to select important nonparametric and parametric components and obtain estimators simultaneously. We also find that the proposed procedure can separate predictors into varying-coefficient and constant-coefficient predictors automatically. Theoretically, it has the selection and estimation consistency properties. Simulation studies and a real data application are conducted to evaluate and illustrate the proposed methods.

Keywords: Efficient estimating equation; Group LASSO; Single-index varying-coefficient model; Variable selection (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10463-020-00757-0

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