Open Access
2016 Bootstrap confidence intervals in functional nonparametric regression under dependence
Paula Raña, Germán Aneiros, Juan Vilar, Philippe Vieu
Electron. J. Statist. 10(2): 1973-1999 (2016). DOI: 10.1214/16-EJS1156
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.

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Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society
Paula Raña, Germán Aneiros, Juan Vilar, and Philippe Vieu "Bootstrap confidence intervals in functional nonparametric regression under dependence," Electronic Journal of Statistics 10(2), 1973-1999, (2016). https://doi.org/10.1214/16-EJS1156
Received: 1 December 2015; Published: 2016
Vol.10 • No. 2 • 2016
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