Dipartimento di Scienze Statistiche

    Il Seminario di Dipartimento del dott. Marco Stefanucci si svolgerà in modalità telematica il giorno 25 NOVEMBRE p.v. con inizio alle ore 12.00 mediante accesso al seguente link: https://uniroma1.zoom.us/j/86128803817?pwd=QStzMzB0ZGJLWFc5Z3FWaUJ0L1Q1QT09  PASSCODE: 395069   Abstract:  In functional data analysis, functional linear regression has attracted significant atten- tion recently. Herein, we consider the case where both the response and covariates are functions. There are two available approaches for addressing such a situation: concurrent and nonconcurrent functional models. In the former, the value of the functional response at a given domain point depends only on the value of the functional regressors evaluated at the same domain point, whereas, in the latter, the functional covariates evaluated at each point of their domain have a non-null effect on the response at any point of its do- main. To balance these two extremes, we propose a locally sparse functional regression model in which the functional regression coefficient is allowed (but not forced) to be ex- actly zero for a subset of its domain. This is achieved using a suitable basis representation of the functional regression coefficient and exploiting an overlapping group-Lasso penalty for its estimation. We introduce efficient computational strategies based on majorization- minimization algorithms and discuss appealing theoretical properties regarding the model support and consistency of the proposed estimator. We further illustrate the empirical performance of the method through simulations and two applications related to human mortality and bidding the energy market."