A sensitivity analysis on pure point and continuous spectral based multivatiate multiple regression approaches
F. D. Miranda Huaynalaya, M. D. Ruiz Medina
A Bayesian maximum a posteriori (MAP) estimation is adopted in functional regression from correlated surfaces (see Ruiz-Medina and Miranda, 2022). As an alternative, spatial curve regression is introduced in the spatial functional spectral domain in this paper. The derived numerical results show a significant impact of the truncation, tapering and smoothing parameters in both approaches. The present talk investigates this impact through a simulation study under different scenarios by a cross-validation analysis. The complexity and high-dimensionality inherent to the infinite-dimensional Bayesian approach makes indispensable a suitable selection criterion. On the other hand, the results from the spatial functional spectral approach are quite sensitive to a suitable choice of the orthonormal basis for projection. Conclusions are drawn for future extensions of both methodologies to alternative estimation frameworks.
Keywords: MAP estimation, nonparametric estimation, spatial curve regression, spatial periodogram operator, spatial spectral density operator, surface regression.
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
N. Mohammadi, V. Panaretos, L. Santoro
A. Torres Signes, M. P. Frías Bustamante, M. D. Ruiz Medina
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6/8/22
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