Friday, September 8, 2017 - 11:00am
Chair of Mathematical Statistics with a Focus on Stochastic Processes Institute of Mathematics, University of Rostock
Nonparametric density estimation for intentionally corrupted functional data
We consider statistical models in which functional data are artificially contaminated by independent Wiener processes in order to satisfy privacy constraints. We show that the corrupted observations have a Wiener density, which determines the distribution of the original functional data uniquely. A nonparametric estimator of the functional density is constructed and its asymptotic properties are studied. We discuss real data applications in the fields of classification and goodness-of-fit testing. This talk is based on a joint work with Aurore Delaigle (University of Melbourne).
24 Hillhouse Avenue, Rm. 107