Shrinkage estimation applied to a semi-nonparametric regression model

被引：0

作者：

Zareamoghaddam, Hossein ^{[1
]}

Ahmed, Syed E. ^{[2
]}

Provost, Serge B. ^{[1
]}

机构：

[1] Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON, Canada

[2] Brock Univ, Math & Stat, St Catharines, ON L2S 3A1, Canada

来源：

INTERNATIONAL JOURNAL OF BIOSTATISTICS | 2021年 / 17卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

local linear regression; multiple simple regression; semi-nonparametric model; shrinkage; BIOMARKER DISCOVERY; MASS-SPECTROMETRY; PARAMETER MATRIX; MULTIVARIATE; SPECTRA; SERUM; TIME;

D O I：

10.1515/ijb-2018-0109

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Stein-type shrinkage techniques are applied to the parametric components of a semi-nonparametric regression model recently proposed by (Ma et al. 2015: 285-303). On the basis of an uncertain prior information (restrictions) about the parameters of interest, shrinkage techniques are shown to improve the accuracy of the model. The effectiveness of the proposed estimators are corroborated by a simulation study.

引用

页码：23 / 38

页数：16

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