Optimal prediction designs in finite discrete spectrum linear regression models

DOI10.1007/s00184-009-0253-4zbMath1200.62075OpenAlexW2024051994MaRDI QIDQ604646

František Štulajter, Radoslav Harman

Publication date: 12 November 2010

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-009-0253-4

zbMATH Keywords

correlated observationsmean square errorbest linear unbiased predictor\(c\)-optimalityrandom coefficients regression

Mathematics Subject Classification ID

Related Items (9)

`Nearly' universally optimal designs for models with correlated observations ⋮ Optimal designs for comparing regression models with correlated observations ⋮ D-optimal designs for mixture experiments with various correlation structures ⋮ Optimal design for linear models with correlated observations ⋮ OPTIMAL DESIGNS FOR SERIES ESTIMATION IN NONPARAMETRIC REGRESSION WITH CORRELATED DATA ⋮ Optimal designs for linear models with Fredholm-type errors ⋮ Optimal designs for the prediction of mixed effects in linear mixed models ⋮ Optimal designs in regression with correlated errors ⋮ Optimal designs for comparing regression curves: dependence within and between groups

Cites Work

• Mean squared error of the empirical best linear unbiased predictor in an orthogonal finite discrete spectrum linear regression model
• Estimation of variances in orthogonal finite discrete spectrum linear regression models
• Equidistant and \(D\)-optimal designs for parameters of Ornstein-Uhlenbeck process
• Elfving's method for \(m\)-dimensional models.
• Computing \(c\)-optimal experimental designs using the simplex method of linear programming
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Mean squared error of empirical predictor.
• An algorithm for the computation of optimum designs under a given covariance structure
• Measures for designs in experiments with correlated errors
• Exactly optimal sampling designs for processes with a product covariance structure
• Best Linear Unbiased Prediction in the Generalized Linear Regression Model
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