Kalman filtering from POP-based diagonalization of ARH(1)

DOI10.1016/j.csda.2006.07.013zbMath1162.62411OpenAlexW1972150774MaRDI QIDQ1020165

Román Salmerón, José M. Angulo, María D. Ruiz-Medina

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.07.013

zbMATH Keywords

dimension reductionoptimal decompositionspatial functional dataautoregressive Hilbertian modelspatiotemporal interaction model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of functional analysis in probability theory and statistics (46N30) Applications of operator theory in probability theory and statistics (47N30)

Related Items (max. 100)

Computing the best linear predictor in a Hilbert space. Applications to general ARMAH processes ⋮ Spatial autoregressive and moving average Hilbertian processes ⋮ Multi-spectral decomposition of functional autoregressive models ⋮ Structural components in functional data ⋮ Functional maximum-likelihood estimation of ARH(\(p\)) models ⋮ Continuous time-varying Kriging for spatial prediction of functional data: an environmental application ⋮ Statistics for functional data ⋮ Recent developments in complex and spatially correlated functional data

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• A family of minimax rates for density estimators in continuous time
• A dimension-reduced approach to space-time Kalman filtering

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