Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions
作者:
Jianqing Fan,
NancyE. Heckman,
M.P. Wand,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 141-150
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476496
出版商: Taylor & Francis Group
关键词: Bandwidth;Boundary effects;Local likelihood;Logistic regression;Nonparametric regression;Poisson regression
数据来源: Taylor
摘要:
We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and quasi-likelihood contexts. In the ordinary regression case, local polynomial fitting has been seen to have several appealing features in terms of intuitive and mathematical simplicity. One noteworthy feature is the better performance near the boundaries compared to the traditional kernel regression estimators. These properties are shown to carry over to generalized linear model and quasi-likelihood settings. We also derive the asymptotic distributions of the proposed class of estimators that allow for straightforward interpretation and extensions of state-of-the-art bandwidth selection methods.
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