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Functional regression and additive models

Mueller, HG (California)
Wednesday 28 May 2008, 11:00-12:00

Seminar Room 1, Newton Institute


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.




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