CMStatistics 2021: Start Registration
View Submission - CMStatistics
Title: Robust optimal estimation of location from discretely sampled functional data Authors:  Ioannis Kalogridis - KU Leuven (Belgium) [presenting]
Stefan Van Aelst - University of Leuven (Belgium)
Abstract: Estimating location is a central problem in functional data analysis. Yet, most current estimation procedures either unrealistically assume completely observed trajectories or lack robustness with respect to the many kinds of anomalies one can encounter in the functional setting. To remedy these deficiencies, we introduce a class of optimal robust location estimators based on discretely sampled functional data. The proposed method is based on M-type smoothing spline estimation with repeated measurements and is suitable for both commonly and independently observed trajectories that are subject to measurement error. We show that under suitable assumptions, the proposed family of estimators is minimax rate optimal both for commonly and independently observed trajectories. We illustrate its highly competitive performance and practical usefulness in a Monte-Carlo study and a real-data example involving recent Covid-19 data.