[Forum SIS] Webinar Yi Yu @Bocconi

[Giacomo Zanella]

We announce the following DEC Statistics webinar:

Date: Thursday, March 11, h17:00 (Italy time)
Speaker: Yi Yu (University of Warwick)
Title: Functional Linear Regression with Mixed Predictors

Abstract: We study a functional linear regression model that deals with
functional responses and allows for both functional covariates and
high-dimensional vector covariates. The proposed model is flexible and
nests several functional regression models in the literature as special
cases. Based on the theory of reproducing kernel Hilbert spaces (RKHS), we
propose a penalized least squares estimator that can accommodate functional
variables observed on discrete grids. Besides the conventional smoothness
penalties, a group Lasso-type penalty is further imposed to induce sparsity
in the high-dimensional vector predictors. We derive finite sample
theoretical guarantees and show that the excess prediction risk of our
estimator is minimax optimal. Furthermore, our analysis reveals an
interesting phase transition phenomenon that the optimal excess risk is
determined jointly by the smoothness and the sparsity of the functional
regression coefficients. A novel efficient optimization algorithm based on
iterative coordinate descent is devised to handle the smoothness and
sparsity penalties simultaneously. Simulation studies and real data
applications illustrate the promising performance of the proposed approach
compared to the state-of-the-art methods in the literature.

The webinar will be on zoom at:
https://zoom.us/j/95651583993?pwd=ZnZjaGxvZWVLdGFSL0dGTk43eU9Idz09
Meeting ID: 956 5158 3993
Passcode: 568279

Kind regards,
Giacomo Zanella
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