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Principal varying coefficient estimator for high-dimensional models
Statistics ( IF 1.9 ) Pub Date : 2019-09-16 , DOI: 10.1080/02331888.2019.1663521
Weihua Zhao 1 , Fode Zhang 2 , Xuejun Wang 3 , Rui Li 4 , Heng Lian 5
Affiliation  

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.

中文翻译:

高维模型的主变系数估计器

摘要 我们考虑高维设置中的主要变系数模型,结合变量选择,以减少半参数建模中的有效参数数量。该估计基于 B 样条方法。对于无惩罚估计量,我们建立估计量的非渐近界,然后建立惩罚估计量的(渐近)局部预言性质,以及非渐近误差界。蒙特卡罗研究揭示了估计器的良好性能,并提出了在真实数据集上的应用。
更新日期:2019-09-16
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