Approximate ML and REML estimation for regression models with spatial or time series AR(1) noise.

DOI10.1016/S0167-7152(02)00437-6zbMath1101.62372OpenAlexW2066597894MaRDI QIDQ1423253

Gregory C. Reinsel, Wai-Kwong Cheang

Publication date: 14 February 2004

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(02)00437-6

zbMATH Keywords

Maximum likelihood estimatorBiasRestricted maximum likelihood estimatorSpatial AR modelTime series regression model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (1)

Improved inference for first-order autocorrelation using likelihood analysis

Cites Work

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• Bias correction in ARMA models
• Closed-Form Approximations to the REML Estimator of a Variance Ratio (or Heritability) in a Mixed Linear Model
• Some new algorithms for computing restricted maximum likelihood estimates of variance components
• Properties of the spatial unilateral first-order ARMA model
• Regression Models with Spatially Correlated Errors
• Finite sample properties of ml and reml estimators in time series regression models with long memory noise
• Bias Reduction of Autoregressive Estimates in Time Series Regression Model through Restricted Maximum Likelihood
• Bayesian inference for variance components using only error contrasts
• ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS

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