Monotonicity preservation properties of kernel regression estimators

Iosif Pinelis

Statistics & Probability Letters, 2021, vol. 177, issue C

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.

Keywords: Kernel regression estimators; Curve fitting; Monotonicity preservation property; Cumulative distribution functions; Quantile functions; Intensity functions of point processes (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016771522100119X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:177:y:2021:i:c:s016771522100119x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2021.109157

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().