Nonparametric Regression, Confidence Regions and Regularization

PL Davies, A Kovac, M Meise

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)

Abstract

In this paper we offer a unified approach to the problem of nonparametricregression on the unit interval. It is based on a universal, honest and nonasymptotic confidence region An which is defined by a set of linear inequalities involving the values of the functions at the design points. Interest will typically center on certain simplest functions in An where simplicity can be defined in terms of shape (number of local extremes, intervals of convexity/concavity) or smoothness (bounds on derivatives) or a combination of both. Once some form of regularization has been decided upon the confidence region can be used to provide honest nonasymptotic confidence bounds which are less informative but conceptually much simpler.
Translated title of the contributionConfidence regions, regularization and non-parametric regression
Original languageEnglish
Pages (from-to)2597 - 2625
Number of pages29
JournalAnnals of Statistics
Volume37
Issue number5B
DOIs
Publication statusPublished - Oct 2009

Bibliographical note

Publisher: IMS

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