Universal Local Linear Kernel Estimators in Nonparametric Regression
Yu. Yu. Linke, I. S. Borisov, Pavel Ruzankin, В. А. Куценко, Е. B. Yarovaya, S. А. Shalnova
Abstract
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. The theoretical results of the study are illustrated by simulations. An example of processing real medical data from the epidemiological cross-sectional study ESSE-RF is included. We compare the new estimators with the estimators best known for such studies.