Automatic Smoothing of Regression Functions in Generalized Linear Models
作者:
Finbarr O'sullivan,
BrianS. Yandell,
WilliamJ. Raynor,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 393
页码: 96-103
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478243
出版商: Taylor & Francis Group
关键词: Penalized likelihood;Smoothing splines;IRLS;Cross-validation
数据来源: Taylor
摘要:
We consider the penalized likelihood method for estimating nonparametric regression functions in generalized linear models (Nelder and Wedderburn 1972) and present a generalized cross-validation procedure for empirically assessing an appropriate amount of smoothing in these estimates. Asymptotic arguments and numerical simulations are used to show that the generalized cross-validatory procedure preforms well from the point of view of a weighted mean squared error criterion. The methodology adds to the battery of graphical tools for model building and checking within the generalized linear model framework. Included are two examples motivated by medical and horticultural applications.
点击下载:
PDF (1041KB)
返 回