Parameter estimation in regression models with autocorrelated errors using irregular data

DOI10.1080/03610929408831465zbMath0825.62246OpenAlexW2002633185MaRDI QIDQ4843857

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Publication date: 17 August 1995

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

Full work available at URL: https://doi.org/10.1080/03610929408831465

zbMATH Keywords

consistencyasymptotic normalityincomplete datamaximum likelihood estimatorleast squares estimatorregressionautocorrelated errors

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Bayesian spatial regression models with closed skew normal correlated errors and missing observations ⋮ Bayesian analysis of regression models with spatially correlated errors and missing observations

Cites Work

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• Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix
• Unit root tests for \(\text{ARIMA}(0,1,q)\) models with irregularly observed samples
• Maximum Likelihood Estimation for Incomplete Repeated-Measures Experiments under an ARMA Covariance Structure
• Asymptotic distribution of parameter estimators for nonconsecutively observed time series
• Estimation of Time Series Models in the Presence of Missing Data
• Exact likelihood of vector autoregressive-moving average process with missing or aggregated data

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