skip to content
 

Functional regression and additive models

Presented by: 
HG Mueller [California]
Date: 
Wednesday 28th May 2008 - 11:00 to 12:00
Venue: 
INI Seminar Room 1
Abstract: 

Functional regression analysis aims at situations where predictors or responses in a regression setting include random functions. Early functional linear models were based on the assumption of observing complete random trajectories, while more recent approaches emphasize more realistic settings of repeated noisy measurements, as encountered in longitudinal studies or online data. Recent joint work with Yao on a functional additive model (FAM) will be discussed. FAM has good asymptotic and practical properties and provides desirable flexibility.

The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons