Automatic smoothing parameter selection in non-parametric models for longitudinal data

被引：0

作者：

Berhane, K ^{[1
]}

Rao, JS

机构：

[1] Univ Pittsburgh, Dept Family Med & Clin Epidemiol, Pittsburgh, PA 15261 USA

[2] Univ Pittsburgh, Dept Biostat, Pittsburgh, PA 15261 USA

[3] Cleveland Clin Fdn, Dept Biostat, Cleveland, OH 44195 USA

来源：

APPLIED STOCHASTIC MODELS AND DATA ANALYSIS | 1997年 / 13卷 / 3-4期

关键词：

generalized estimating equations; quasi-likelihood; correlated data; local-scoring; smoothing; cross validation; BRUTO;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

The selection of smoothing parameters by generalized cross-validation (GCV) becomes complicated when dealing with correlated data. In this paper, we develop an automatic algorithm for selection of smoothing parameters in non-parametric longitudinal models by combining the BRUTO algorithm of Hastie (1989) and the modifications to GCV due to Altman (1990) to handle the correlation. The algorithm is detailed and illustrated via analysis of a panic-attack data set. (C) 1998 John Wiley & Sons, Ltd.

引用

页码：289 / 296

页数：8

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