Marginal longitudinal semiparametric regression via penalized splines

被引：3

作者：

Al Kadiri, M. ^{[1
]}

Carroll, R. J. ^{[2
]}

Wand, M. P. ^{[1
]}

机构：

[1] Univ Wollongong, Sch Math & Appl Stat, Ctr Stat & Survey Methodol, Wollongong, NSW, Australia

[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2010年 / 80卷 / 15-16期

基金：

澳大利亚研究理事会;

关键词：

Additive models; Best prediction; Maximum likelihood; Gibbs sampling; Nonparametric regression; Restricted maximum likelihood; Varying coefficient models; NONPARAMETRIC REGRESSION; CLUSTERED DATA; MODELS; KERNEL;

D O I：

10.1016/j.spl.2010.04.002

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1242 / 1252

页数：11

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