论文标题
非参数回归中的通用局部线性内核估计器
Universal local linear kernel estimators in nonparametric regression
论文作者
论文摘要
为广泛的非参数回归模型提出了新的本地线性估计器。不管满足依赖设计元素的传统条件，估计器都一致一致。 估计器是特殊加权最小二乘法的解决方案。该设计可以是固定的，也可以随机的，不需要满足经典的规律性或独立条件。作为应用程序，为密集功能数据的平均值构建了几个估计器。该研究的理论结果通过模拟说明。包括流行病学横断面研究ESER-RF处理真实医学数据的一个示例。我们将新的估计量与以这种研究闻名的估计器进行比较。
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 depen\-dence 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.
