Confidence and prediction intervals in semi-functional partial linear regression

Semi-functional partial linear regression model allows to deal with a non-parametric and a linear component within the functional regression. Naive and wild bootstrap procedures are proposed to approximate the distribution of the estimators for each component in the model, and their asymptotic validities are obtained in the context of dependence data, under a-mixing conditions. Based on that bootstrap procedures, confidence intervals can be obtained for each component in the model, which can be also extended to deal with prediction intervals and prediction densities.