Montag, 29. Oktober 2018, 16:45 - 17:45 iCal

ISOR Colloquium

"On the Optimal Reconstruction of Partially Observed Functional Data"

Speaker: Alois Kneip (Univ. Bonn)

HS 7 OMP1 (#1.303)
Oskar-Morgenstern-Platz 1, 1090 Wien


We propose a new reconstruction operator that aims to recover the missing parts of a function given the observed parts. This new operator belongs to a new, very large class of functional operators which includes the classical regression operators as a special case. We show the optimality of our reconstruction operator and demonstrate that the usually considered regression operators generally cannot be optimal reconstruction operators. Our estimation theory allows for autocorrelated functional data and considers the practically relevant situation in which each of the n functions is observed at m discretization points. We derive rates of consistency for our nonparametric estimation procedures using a double asymptotic. For data situations, as in our real data application where m is considerably smaller than n, we show that our functional principal components based estimator can provide better rates of convergence than any conventional nonparametric smoothing method.

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Institut für Statistik und Operations Research


Mag. Vera Lehmwald
Fakultät für Wirtschaftswissenschaften
Institut für Statistik und Operations Research
+43 1 4277 38651