Title: High-dimensional function-on-scalar regression in Hilbert spaces
Authors: Matthew Reimherr - Pennsylvania State University (United States) [presenting]
Alice Parodi - Politecnico Di Milano (Italy)
Abstract: Recent work concerning function-on-scalar regression when the number of predictors is much larger than the sample size is discussed. In particular, we will present a new methodology, called FLAME for Functional Linear Adaptive Mixed Estimation, which simultaneously selects, estimates, and smooths the important predictors in the model. Our methodology is readily available as an R package that utilizes a coordinate descent algorithm for fast implementation. Asymptotic theory will be provided and we will compare to previous methods via simulations.