Least upper bound for the covariance matrix of a generalized least squares estimator in regression with applications to a seemingly unrelated regression model and a heteroscedastic model

DOI10.1214/aos/1032298283zbMath0868.62060OpenAlexW1966724189MaRDI QIDQ1354380

Takeaki Kariya, Hiroshi Kurata

Publication date: 5 May 1997

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1032298283

zbMATH Keywords

efficiencycovariance matrixKantorovich inequalityleast upper boundGauss-Markov estimatorheteroscedastic modelgeneralized least squares estimatorseemingly unrelated regression modelZellner estimatorgeneral normal regression modelnonlinear Gauss-Markov theorem

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05)

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Cites Work

• An approach to upper bound problems for risks of generalized least squares estimators
• Bounds for the Covariance Matrices of Zellner's Estimator in the SUR Model and the 2SAE in a Heteroscedastic Model
• Some Finite Sample Results in the Context of Two Seemingly Unrelated Regression Equations
• Estimators for Seemingly Unrelated Regression Equations: Some Exact Finite Sample Results
• On the Distribution of the Largest Latent Root and the Corresponding Latent Vector for Principal Component Analysis
• Joint Distribution of the Extreme Roots of a Covariance Matrix
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
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