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UID:calendar.135315.field_date.0@www.tse-fr.eu
DTSTAMP:20260414T175622Z
CREATED:20240710T211001Z
DESCRIPTION:Marine Carrasco (University of Montreal)\, “Functional Partial 
 Least-Squares: Optimal Rates and Adaptation”\, Econometrics and Empirical 
 Economics Seminar\, TSE\, November 5\, 2024\, 15:30–16:50\, room Auditoriu
 m 4.\n\nWe consider the functional linear regression model with a scalar r
 esponse and a\nHilbert space-valued predictor\, a well-known ill-posed inv
 erse problem. We propose\na new formulation of the functional partial leas
 t-squares (PLS) estimator related to\nthe conjugate gradient method. We pr
 ovide the first optimality result for functional\nPLS showing that the est
 imator achieves the (nearly) optimal convergence rate on a\nclass of ellip
 soids and propose a data-driven early stopping rule that adapts to the\nun
 known degree of ill-posedness. We find in simulations that our estimator p
 erforms\nfavorably compared to the principal component regression estimato
 r and requires a\nsmaller number of functional components. Using our estim
 ator\, we study the nonlinear\ntemperature effect on corn and soybean yiel
 ds and find some evidence of the\nadaptation of US agriculture to climate 
 change.
DTSTART;TZID=Europe/Paris:20241105T153000
DTEND;TZID=Europe/Paris:20241105T165000
LAST-MODIFIED:20260113T095129Z
LOCATION:TSE\, November 5\, 2024\, 15:30–16:50\, room Auditorium 4
SUMMARY:Econometrics and Empirical Economics Seminar
URL;TYPE=URI:https://www.tse-fr.eu/seminars/2024-functional-partial-least-s
 quares-optimal-rates-and-adaptation
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