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.
"Bootstrap confidence intervals in functional nonparametric regression under dependence." Electron. J. Statist. 10 (2) 1973 - 1999, 2016. https://doi.org/10.1214/16-EJS1156