Cari Colleghi, A seguire annuncio di un seminario organizzato presso il Laboratorio MOX - Politecnico di Milano Hedayat Fathi, Université Laval, Québec 10.07.26 ore 11:30 – Aula Saleri Link: https://mox.polimi.it/mox-colloquia-seminars-list/mox-seminars/?id_evento=27... Titolo: SOFIA and MIP-FoSR: Two Methods for Variable Selection in Functional Linear Regression Abstract: This presentation covers two recent contributions to variable selection in functional linear regression. For the scalar-on-function setting, we propose SOFIA (Scalar-On-Function Integrated Adaptive Lasso). We assume the functional covariates are in a Hilbert space while the coefficient functions belong to a specific subspace of it, such as a reproducing kernel Hilbert space. In this way, we impose a controlled level of smoothness or periodicity on the coefficients. The method satisfies a functional oracle property even when the number of predictors exceeds the sample size. For the function-on-scalar setting, we propose MIP-FoSR, a mixed-integer programming framework that performs simultaneous variable selection and outlier detection. It extends the "mean-shift outlier model" to the functional setting, and uses grouped binary indicators on basis-expansion coefficients to impose explicit bounds on the number of selected predictors and detected outliers. We establish an equivalence with a functional sparse trimming problem, derive a finite-sample breakdown point, and prove a functional robust strong oracle property. Un caro saluto a tutti, Laura Sangalli —— Laura Maria Sangalli MOX - Dipartimento di Matematica Politecnico di Milano Piazza Leonardo da Vinci 32 20133 Milano - Italy (+39) 02 2399 4554 laura.sangalli@polimi.it https://sangalli.faculty.polimi.it