Monotone function estimation in partially linear models

被引：0
|
作者：
Zhang, Yi [1 ,2 ]
Wang, Shaoli [3 ]
机构：
[1] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
[2] Shanghai Lixin Univ Accounting & Finance, Sch Insurance, Shanghai 201209, Peoples R China
[3] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai Key Lab Financial Informat Technol, Shanghai 200433, Peoples R China
来源：
STATISTICS AND ITS INTERFACE | 2018年 / 11卷 / 01期
关键词：
Asymptotic normality; Density estimation; Kernel estimation; Monotone function; Nonparametric function; Partially linear models; BARDET-BIEDL-SYNDROME; REGRESSION FUNCTION; LIKELIHOOD;
D O I：
10.4310/SII.2018.v11.n1.a2
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
A kernel-based method is proposed for the monotone estimation of the nonparametric function component of a partially linear regression model. The estimated monotone function is constructed via a density estimate and numerical inversion. This procedure does not require constrained optimization and hence is fast to compute. Asymptotic normality is established for the proposed monotone function estimator. We apply the proposed method to analyze mammalian eye gene expression data and reveal a complex nonlinear relation within a gene network; we also analyze the German SOEP data using our method and validate the human capital theory.
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页码：19 / 29
页数：11
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