ADAPTIVE SEMI-VARYING COEFFICIENT MODEL SELECTION

被引：86

作者：

Hu, Tao ^{[1
,3
]}

Xia, Yingcun ^{[2
]}

机构：

[1] Guizhou Coll Finance & Econ, Guiyang, Peoples R China

[2] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117546, Singapore

[3] Capital Normal Univ, Dept Math, Beijing, Peoples R China

来源：

STATISTICA SINICA | 2012年 / 22卷 / 02期

关键词：

BIC; kernel smoothing; LASSO; model selection; oracle property; SCAD; semi-varying coefficient model; NONCONCAVE PENALIZED LIKELIHOOD; VARIABLE SELECTION; EFFICIENT ESTIMATION; REGRESSION; LASSO; SHRINKAGE; INFERENCES;

D O I：

10.5705/ss.2010.105

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

070103 [概率论与数理统计]; 140311 [社会设计与社会创新];

摘要：

Identification of constant coefficients in a semi-varying coefficient model is an important issue (Zhang et al (2002)). We propose a novel method for this by combining local polynomial smoothing (Fan and Zhang (1999)) with shrinkage estimation (Tibshirani (1996)). Unlike the stepwise procedure (Xia, Zhang, and Tong (2004)), our method can identify the constant coefficients and estimate the model simultaneously. By imposing the adaptive LASSO penalty and starting with the Nadaraya-Watson estimator, the method can identify the constant coefficients and varying coefficients consistently, and estimate the model with oracle efficiency (Fan and Li (2001)).

引用

页码：575 / 599

页数：25

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