Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Kalman filtering from POP-based diagonalization of ARH(1)
Computational Statistics & Data Analysis
Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications
Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications
Cokriging for spatial functional data
Journal of Multivariate Analysis
Tensorial products of functional ARMA processes
Journal of Multivariate Analysis
| Hi-index | 0.00 |
This paper addresses the introduction and study of structural properties of Hilbert-valued spatial autoregressive processes (SARH(1) processes), and Hilbert-valued spatial moving average processes (SMAH(1) processes), with innovations given by two-parameter (spatial) matingale differences. For inference purposes, the conditions under which the tensorial product of standard autoregressive Hilbertian (ARH(1)) processes (respectively, of standard moving average Hilbertian (MAH(1)) processes) is a standard SARH(1) process (respectively, it is a standard SMAH(1) process) are studied. Examples related to the spatial functional observation of two-parameter Markov and diffusion processes are provided. Some open research lines are described in relation to the formulation of SARMAH processes, as well as General Spatial Linear Processes in Functional Spaces.