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

A goodness-of-fit test for functional time series

J. Álvarez Liébana, W. González Manteiga, M. D. Ruiz Medina

Nonparametric estimation of covariance and autocovariance operators

A. Caponera, J. Fageot, M. Simeoni, V. M. Panaretos

Model selection in dynamical multivariate spherical curve regression

A. Torres Signes, M. P. Frías Bustamante, M. D. Ruiz Medina


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