Non-Parametric Conditional U-Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design

Stute presented the so-called conditional U-statistics generalizing the Nadaraya–Watson estimates of the regression function. Stute demonstrated their pointwise consistency and the asymptotic normality. In this paper, we extend the results to a more abstract setting. We develop an asymptotic theory...

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Bibliographic Details
Published in:	Mathematics Vol. 11; no. 1; p. 16
Main Authors:	Bouzebda, Salim, Soukarieh, Inass
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2023 MDPI
Subjects:	[MATH.MATH-FA]Mathematics [math]/Functional Analysis [math.FA] [MATH.MATH-PR]Mathematics [math]/Probability [math.PR] [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] Asymptotic properties conditional U-processes conditional U-statistics conditional U-statistics; locally stationary random field; functional data; empirical processes; conditional U-processes; VC-class of functions; kernel-type estimators; regression; irregularly spaced data Convergence Data analysis empirical processes Fields (mathematics) Functional Analysis functional data Functionals locally stationary random field Mathematics Nonparametric statistics Normality Probability QA1-939 Random variables Sampling designs Spatial data Statistics Statistics Theory Stochastic processes VC-class of functions
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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