Bounds for normal approximations to the distributions of generalized least squares predictors and estimators

DOI10.1016/0378-3758(92)90082-4zbMath0746.62014OpenAlexW1971470736MaRDI QIDQ1193986

Yasuyuki Toyooka, Takeaki Kariya

Publication date: 27 September 1992

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

Full work available at URL: https://doi.org/10.1016/0378-3758(92)90082-4

zbMATH Keywords

conditional distributionsgeneralized least squares estimatorsfirst-order autoregressive errorsGLS predictorsnormal approximation with uniform bounds

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (2)

Exact Results on the Inadmissibility of the Feasible Generalized Least Squares Estimator in Regression Models with Non-Spherical Disturbances ⋮ Improved multivariate prediction in a general linear model with an unknown error covariance matrix.

Cites Work

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• Second-order risk structure of GLSE and MLE in a regression with a linear process
• An approach to upper bound problems for risks of generalized least squares estimators
• Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix
• Bounds for the Covariance Matrices of Zellner's Estimator in the SUR Model and the 2SAE in a Heteroscedastic Model
• Prediction error in a linear model with estimated parameters
• The Asymptotic Mean Squared Error of Multistep Prediction from the Regression Model with Autoregressive Errors
• Best Linear Unbiased Prediction in the Generalized Linear Regression Model

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