We consider functional data where an underlying smooth curve is composed not just
with errors, but also with irregular spikes that (a) are themselves of interest, and (b)
can negatively affect our ability to characterize the underlying curve. We propose an
approach that, combining regularized spline smoothing and an ExpectationMaximization algorithm, allows one to both identify spikes and estimate the smooth
component. Imposing some assumptions on the error distribution, we prove
consistency of EM estimates. Next, we demonstrate the performance of our proposal
on finite samples and its robustness to assumptions violations through simulations.
Finally, we apply our proposal to data on the annual heatwaves index in the US and
on weekly electricity consumption in Ireland. In both datasets, we are able to
characterize underlying smooth trends and to pinpoint irregular/extreme behaviors.
Work in collaboration with Huy Dang (Penn State University) and Francesca
Chiaromonte (Penn State University and Sant’Anna School of Advanced Studies).
10/03/2023
The seminar is given by Marzia A. Cremona
Université Laval Department of operations and decision systems at the following zoom link https://uniroma1.zoom.us/j/86881977368?pwd=SWRFcVFjMDZTa0lXZk05TE1zNm5adz09