On the quality of likelihood-based estimators in spatial autoregressive models when the data dependence structure is misspecified

DOI10.1016/S0378-3758(97)00156-0zbMath0953.62102OpenAlexW2067832185MaRDI QIDQ1299480

Francesco Lagona, Daniel A. Griffith

Publication date: 29 January 2001

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(97)00156-0

zbMATH Keywords

maximum likelihood estimationmixingspatial autoregression

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (5)

Spatial correlation robust inference with errors in location or distance ⋮ Edge effect in disease mapping ⋮ On a Semiparametric Data‐Driven Nonlinear Model with Penalized Spatio‐Temporal Lag Interactions ⋮ THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS ⋮ Selection of the neighborhood structure for space-time Markov random field models

Uses Software

• spatial

Cites Work

• Consistent maximum-likelihood estimation with dependent observations. The general (nonnormal) case and the normal case
• Interaction levels in an autoregressive graphical model
• Asymptotics for REML estimation of spatial covariance parameters
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Edge effects and efficient parameter estimation for stationary random fields
• Parameter estimation for a stationary process on a d-dimensional lattice
• Estimation Methods for Models of Spatial Interaction
• Efficiency of least squares estimators in the presence of spatial autocorrelation
• IV.—On Least Squares and Linear Combination of Observations
• When are Gauss-Markov and Least Squares Estimators Identical? A Coordinate-Free Approach
• Note on the Consistency of the Maximum Likelihood Estimate
• ON STATIONARY PROCESSES IN THE PLANE
• Statistical Inference for Spatial Processes
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