Monotonicity preservation properties of kernel regression estimators

被引：2

作者：

Pinelis, Iosif ^{[1
]}

机构：

[1] Michigan Technol Univ, Dept Math Sci, Houghton, MI 49931 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2021年 / 177卷

关键词：

Kernel regression estimators; Curve fitting; Monotonicity preservation property; Cumulative distribution functions; Quantile functions; Intensity functions of point processes;

D O I：

10.1016/j.spl.2021.109157

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Three common classes of kernel regression estimators are considered: the Nadaraya-Watson (NW) estimator, the Priestley-Chao (PC) estimator, and the Gasser-Muller (GM) estimator. It is shown that (i) the GM estimator has a certain monotonicity preservation property for any kernel K, (ii) the NW estimator has this property if and only the kernel K is log concave, and (iii) the PC estimator does not have this property for any kernel K. Other related properties of these regression estimators are discussed. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：7

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