Maximum likelihood estimation with missing spatial data and with an application to remotely sensed data

DOI10.1080/03610928908830008zbMath0696.62117OpenAlexW1990148821WikidataQ58810651 ScholiaQ58810651MaRDI QIDQ3473099

Robert Haining, Robert J. Bennett, Daniel A. Griffith

Publication date: 1989

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

Full work available at URL: https://doi.org/10.1080/03610928908830008

zbMATH Keywords

maximum likelihood estimatormissing dataspatial model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10)

Related Items (6)

Information loss on the dependence parameter due to incomplete data from a one-parameter spatial gaussian process ⋮ Estimation and smoothing from incomplete data for a class of lattice processes ⋮ Application of em-type algorithms to spatial data ⋮ Stochastic complexity and model selection from incomplete data ⋮ Information loss on the mean for spatial processes when some values are missing ⋮ Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland

Cites Work

• Unnamed Item
• A proposal for handling missing data
• Kriging Nonstationary Data
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Exact maximum likelihood for incomplete data from a correlated gaussian process
• Characterizing the Estimation of Parameters in Incomplete-Data Problems
• Inference and missing data
• On the estimation and testing of spatial interaction in Gaussian lattice processes
• Missing Observations in Multivariate Statistics. III: Large Sample Analysis of Simple Linear Regression

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