Super-Consistent Estimation of Points of Impact in Nonparametric Regression with Functional Predictors
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Predicting scalar outcomes using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time-points of the functional predictors have an impact on the outcome. Such points of impact are typically unknown and have to be estimated besides the usual model components. In this paper we consider the case of nonparametric models and the practically relevant case of generalized linear models. We show that our point of impact estimators enjoy a super-consistent convergence rate and do not require knowledge or pre-estimates of the model components. This remarkable result facilitates the subsequent estimation of the remaining model components as shown in the theoretical part. The finite sample properties of our estimators are assessed by means of a simulation study. Our methodology is motivated by data from a psychological experiment in which the participants were asked to continuously rate their emotional state while watching an affective video eliciting varying intensity of emotional reactions.
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