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SIAM Chapter Seminar

Mattia Ciollaro
Carnegie Mellon University (Statistics)
Title: Nonparametric functional regression and mode hunting in function spaces

Abstract: Functional Data Analysis (FDA) is an exciting area of Statistics that has been facing rapid evolution and increasing interest in the last decade. FDA techniques are designed to deal with data that are intrinsically infinite dimensional and vary over a continuum (e.g. curves, surfaces, images, ...). This type of data arises more and more frequently in various fields of applied sciences (chemometrics, biometrics, physics, econometrics, ...) and its infinite-dimensional nature poses new challenges that require the development of novel and efficient statistical techniques. While parametric functional models attracted most of the attention in the past, more recently a strong interest emerged in the development of flexible nonparametric functional techniques. In this talk, we will focus on two nonparametric functional problems:

- regression when both the predictor and the response variable are smooth functions

- mode hunting in spaces of smooth functions.

Along with a statistical overview of these problems, we will also present results on real data from our current research.

Date: Thursday, March 6, 2014
Time: 5:30 pm
Location: Wean Hall 8220
Submitted by:  Matteo Rinaldi