Improved multivariate prediction in a general linear model with an unknown error covariance matrix.

DOI10.1006/jmva.2001.2042zbMath1043.62059OpenAlexW2056384711MaRDI QIDQ1867141

Alan T. K. Wan, Anoop Chaturvedi, Shri Prakash Singh

Publication date: 2 April 2003

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.2001.2042

zbMATH Keywords

predictiontablesrisklarge sample asymptoticsquadratic lossStein-rule

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (12)

Admissibility of simultaneous prediction for actual and average values in finite population ⋮ A note on the properties of Stein-rule and inequality restricted estimators when the regression model is over-fitted ⋮ Quadratic prediction problems in finite populations ⋮ An algebraic study of BLUPs under two linear random-effects models with correlated covariance matrices ⋮ Simultaneous prediction using target function based on principal components estimator with correlated errors ⋮ Improved preliminary test and Stein-rule Liu estimators for the ill-conditioned elliptical linear regression model ⋮ On quadratic prediction problems in finite populations ⋮ Simultaneous prediction in the generalized linear model ⋮ Quadratic prediction problems in multivariate linear models ⋮ Robustness of Stein-type estimators under a non-scalar error covariance structure ⋮ Simultaneous optimal predictions under two seemingly unrelated linear random-effects models ⋮ Permutation Matrix with Application to Optimal Invariant Quadratic Prediction in Finite Populations

Cites Work

• Bounds for normal approximations to the distributions of generalized least squares predictors and estimators
• Errate of Kariya and Toyooka (1992), Bounds for normal approximations to the distributions of generalized least squares predictors and estimators
• Operational variants of the minimum mean squared error estimator in linear regression models with non-spherical disturbances
• Linear models. Least squares and alternatives
• Improved multivariate prediction under a general linear model
• Predictive Performance of the Methods of Restricted and Mixed Regression Estimators
• Improved Predictions in Linear Regression Models with Stochastic Linear Constraints
• Double \(k\)-class estimators in regression models with non-spherical disturbances
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