STA 290 Seminar: Alois Kneip

STA 290 Seminar Series

DATE:             Thursday, May 11th 2017, 4:10pm

LOCATION:    MSB 1147, Colloquium Room. Refreshments at 3:30pm in MSB 4110

SPEAKER:     Alois Kneip, University of Bonn, Germany

TITLE:             On the Optimal Reconstruction of Partially Observed Functional Data”

ABSTRACT:   We propose a new linear prediction operator that aims to recover the missing parts of a function given the observed parts. The structure of an optimal linear predictor is analyzed theoretically. Our estimation theory allows for autocorrelated functional data and considers the practically relevant situation where each function (in total $n$ many) is observed at $m$ discretization points. We derive uniform rates of consistency for our nonparametric estimation procedures using a double asymptotic that allows investigate all data scenarios from almost sparse to dense functional data. The finite sample properties are investigated through simulations and a real data application.