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Local polynomial kernel regression for generalized linear models and quasi-likelihood functions

Jianqing Fan Nancy E Heckman Matt P Wand

Statistics Theory and Methods mathscidoc:1912.43265

Journal of the American Statistical Association, 90, (429), 141-150, 1995.3
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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@inproceedings{jianqing1995local,
  title={Local polynomial kernel regression for generalized linear models and quasi-likelihood functions},
  author={Jianqing Fan, Nancy E Heckman, and Matt P Wand},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113351132286825},
  booktitle={Journal of the American Statistical Association},
  volume={90},
  number={429},
  pages={141-150},
  year={1995},
}
Jianqing Fan, Nancy E Heckman, and Matt P Wand. Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. 1995. Vol. 90. In Journal of the American Statistical Association. pp.141-150. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113351132286825.
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