Model selection in dynamical multivariate spherical curve regression
A. Torres Signes, M. P. Frías Bustamante, M. D. Ruiz Medina
An intrinsic dynamical multivariate functional regression from manifold-valued curve data is presented, inspired on Fréchet regression. A regularization scheme, based on Rigged-Hilbert spaces and Kuelb’s lemma, is adopted to compute Fréchet weights from the log-mapped curve regressors in the tangent space. The manifold-valued curve regressors are then selected at each time, from a set of candidates, by minimizing the Fréchet weighted sum of the supremum geodesic distances of the spherical curve response to the elements of the candidate regressor set. A simulation study is carried out to test the suitability of the model selection procedure, within a family of time-correlated spherical curves generated by applying inverse Von Mises-Fisher transform to a family of vector processes, simulated from matrix differential stochastic equations.
Keywords: Dynamical Fréchet-like spherical curve regression, manifold-valued correlated curve data, functional model selection
Scheduled
GT06 Functional Data Analysis IV. Functional Time Series
June 9, 2022 10:10 AM
Grade Hall
Other papers in the same session
J. Álvarez Liébana, W. González Manteiga, M. D. Ruiz Medina
A. Caponera, J. Fageot, M. Simeoni, V. M. Panaretos
F. D. Miranda Huaynalaya, M. D. Ruiz Medina
N. Mohammadi, V. Panaretos, L. Santoro
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