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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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

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

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

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Zentralblatt MATH identifier

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

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


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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