Estimation and smoothing from incomplete data for a class of lattice processes

DOI10.1080/03610929608831720zbMath0875.62443OpenAlexW1981011207MaRDI QIDQ4337147

M. C. Bueso, José M. Angulo, Francisco Javier Alonso

Publication date: 11 November 1997

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929608831720

zbMATH Keywords

predictionsmoothingEM algorithmincomplete datamaximum likelihoodlattice processes

Mathematics Subject Classification ID

Inference from stochastic processes (62M99)

Related Items (4)

Application of em-type algorithms to spatial data ⋮ Goodness-of-fit tests for the spatial spectral density ⋮ Spatial sampling design based on stochastic complexity. ⋮ Stochastic complexity and model selection from incomplete data

Uses Software

• GSLIB

Cites Work

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• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• An introduction to stochastic processes and their applications
• Stochastic processes and filtering theory
• Statistical spatial series modelling II: Some further results on unilateral lattice processes
• Information loss on the mean for spatial processes when some values are missing
• Properties of the spatial unilateral first-order ARMA model
• Maximum likelihood estimation with missing spatial data and with an application to remotely sensed data
• Information loss due to incomplete data from a spatial gaussian one-parameter first-order conditional process
• Restoration of Noisy Images Using a Two-Dimensional Linear Model
• Kalman filtering in two dimensions
• Statistical spatial series modelling
• A subclass of lattice processes applied to a problem in planar sampling

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