Monotonicity preservation properties of kernel regression estimators
Document Type
Article
Publication Date
10-2021
Department
Department of Mathematical Sciences
Abstract
Three common classes of kernel regression estimators are considered: the Nadaraya–Watson (NW) estimator, the Priestley–Chao (PC) estimator, and the Gasser–Müller (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.
Publication Title
Statistics & Probability Letters
Recommended Citation
Pinelis, I.
(2021).
Monotonicity preservation properties of kernel regression estimators.
Statistics & Probability Letters,
177.
http://doi.org/10.1016/j.spl.2021.109157
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14967
