Universal Local Linear Kernel Estimators in Nonparametric Regression

@article{Linke2022UniversalLL,
  title={Universal Local Linear Kernel Estimators in Nonparametric Regression},
  author={Yu. Yu. Linke and Igor' Semenovich Borisov and Pavel S. Ruzankin and Vladimir Kutsenko and Elena Yarovaya and S. A. Shalnova},
  journal={Mathematics},
  year={2022}
}
New local linear estimators are proposed for a wide class of nonparametric regression models. The estimators are uniformly consistent regardless of satisfying traditional conditions of dependence of design elements. The estimators are the solutions of a specially weighted least-squares method. The design can be fixed or random and does not need to meet classical regularity or independence conditions. As an application, several estimators are constructed for the mean of dense functional data… 

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