Functional regression and additive models
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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