Electronic Journal of Statistics

Bootstrap confidence intervals in functional nonparametric regression under dependence

Paula Raña, Germán Aneiros, Juan Vilar, and Philippe Vieu

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Abstract

This paper considers naive and wild bootstrap procedures to construct pointwise confidence intervals for a nonparametric regression function when the predictor is of functional nature and when the data are dependent. Assuming $\alpha$-mixing conditions on the sample, the asymptotic validity of both procedures is obtained. A simulation study shows promising results when finite sample sizes are used, while an application to electricity demand data illustrates its usefulness in practice.

Article information

Source
Electron. J. Statist., Volume 10, Number 2 (2016), 1973-1999.

Dates
Received: December 2015
First available in Project Euclid: 18 July 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1468849968

Digital Object Identifier
doi:10.1214/16-EJS1156

Mathematical Reviews number (MathSciNet)
MR3522666

Zentralblatt MATH identifier
1346.62082

Subjects
Primary: 62G08: Nonparametric regression 62G09: Resampling methods 62G20: Asymptotic properties

Keywords
Functional data bootstrap nonparametric regression confidence intervals $\alpha$-mixing

Citation

Raña, Paula; Aneiros, Germán; Vilar, Juan; Vieu, Philippe. Bootstrap confidence intervals in functional nonparametric regression under dependence. Electron. J. Statist. 10 (2016), no. 2, 1973--1999. doi:10.1214/16-EJS1156. https://projecteuclid.org/euclid.ejs/1468849968


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