Exact maximum likelihood for incomplete data from a correlated gaussian process
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Publication:3345635
DOI10.1080/03610928408828754zbMath0552.62066OpenAlexW2128714506MaRDI QIDQ3345635
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Publication date: 1984
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610928408828754
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Maximum likelihood estimation with missing spatial data and with an application to remotely sensed data ⋮ Some results on unilateral ARMA lattice processes ⋮ Information loss due to incomplete data from a spatial gaussian one-parameter first-order conditional process ⋮ Interpolation of spatial and spatio-temporal Gaussian fields using Gaussian Markov random fields ⋮ Robust methods for the analysis of spatially autocorrelated data ⋮ Efficient likelihood computations for some multivariate Gaussian Markov random fields ⋮ Leverage, influence and residuals in regression models when observations are correlated ⋮ Approximations to the determinant term in gaussian maximum likelihood estimation of some spatial models
Cites Work
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- A Monte Carlo study of autoregressive integrated moving average processes
- Maximum likelihood estimation of models for residual covariance in spatial regression
- Parameter estimation for a stationary process on a d-dimensional lattice
- Inference and missing data
- Testing for Autocorrelation with Missing Observations
- A subclass of lattice processes applied to a problem in planar sampling
- ON STATIONARY PROCESSES IN THE PLANE
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