Efficient estimation and inferences for varying-coefficient models

Zongwu Cai Jianqing Fan Runze Li

Statistics Theory and Methods mathscidoc:1912.43261

Journal of the American Statistical Association, 95, (451), 888-902, 2000.9
This article deals with statistical inferences based on the varying-coefficient models proposed by Hastie and Tibshirani. Local polynomial regression techniques are used to estimate coefficient functions, and the asymptotic normality of the resulting estimators is established. The standard error formulas for estimated coefficients are derived and are empirically tested. A goodness-of-fit test technique, based on a nonparametric maximum likelihood ratio type of test, is also proposed to detect whether certain coefficient functions in a varying-coefficient model are constant or whether any covariates are statistically significant in the model. The null distribution of the test is estimated by a conditional bootstrap method. Our estimation techniques involve solving hundreds of local likelihood equations. To reduce the computational burden, a one-step Newton-Raphson estimator is proposed and implemented. The resulting one
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@inproceedings{zongwu2000efficient,
  title={Efficient estimation and inferences for varying-coefficient models},
  author={Zongwu Cai, Jianqing Fan, and Runze Li},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113333482272821},
  booktitle={Journal of the American Statistical Association},
  volume={95},
  number={451},
  pages={888-902},
  year={2000},
}
Zongwu Cai, Jianqing Fan, and Runze Li. Efficient estimation and inferences for varying-coefficient models. 2000. Vol. 95. In Journal of the American Statistical Association. pp.888-902. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113333482272821.
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