This paper deals with the semi-functional partial linear regression model
Y = X T β +m(χ)+ε under α-mixing conditions. β ∈ R p and m(·) denote an unknown
vector and an unknown smooth real-valued operator, respectively. The covariates X
and χ are valued in R p and some infinite-dimensional space, respectively, and the
random error ε verifies E(ε|X, χ ) = 0. Naïve and wild bootstrap procedures are
proposed to approximate the distribution of kernel-based estimators of β and m(χ ),
and their asymptotic validities are obtained. A simulation study shows the behavior (on
finite sample sizes) of the proposed bootstrap methodology when applied to construct
confidence intervals, while an application to real data concerning electricity market
illustrates its usefulness in practice.

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• Bootstrap in semi-functional partial linear regression under dependence