A0707
Title: Extremile scalar-on-function regression with application to climate scenarios
Authors: Maria Laura Battagliola - Instituto Tecnologico Autonomo de Mexico (Mexico) [presenting]
Martin Bladt - University of Copenhagen (Denmark)
Abstract: Extremiles provide a generalization of quantiles which are not only robust but also have an intrinsic link with extreme value theory. An extremile regression model is introduced tailored for functional covariate spaces. The estimation procedure turns out to be a weighted version of local linear scalar-on-function regression, where now a double kernel approach plays a crucial role. Asymptotic expressions for the bias and variance are established, applicable to both decreasing bandwidth sequences and automatically selected bandwidths. The methodology is then investigated in detail through a simulation study. Furthermore, the applicability of the model is highlighted through the analysis of data sourced from the CH2018 Swiss climate scenarios project, offering insights into its ability to serve as a modern tool to quantify climate behavior.
