Determinant formulas with applications to designing when the observations are correlated

DOI10.1007/BF00773469zbMath0833.62070MaRDI QIDQ1901685

Wolfgang Bischoff

Publication date: 8 November 1995

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

zbMATH Keywords

correlated observationsleast squares estimatorefficiency boundsgeneral linear modelmultiple response\(D\)-criteriondeterminant formulasmaximin designsefficiency of designsGauß-Markov estimatortridiagonal covariance matrices

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Optimal statistical designs (62K05)

Related Items (9)

Optimal allocation of time points for the random effects model ⋮ On maximin designs for correlated observations ⋮ On efficient robust first order rotatable designs with autocorrelated error ⋮ Robust first-order rotatable lifetime improvement experimental designs ⋮ Optimum designs for a multiresponse regression model ⋮ Optimal designs for dual response polynomial regression models ⋮ On \(D\)-optimal robust second order slope-rotatable designs ⋮ On \(D\)-optimal robust designs for lifetime improvement experiments ⋮ Optimal Designs for Multi-Response Nonlinear Regression Models With Several Factors via Semidefinite Programming

Cites Work

• On D-optimal designs for linear models under correlated observations with an application to a linear model with multiple response
• Optimum and minimax exact treatment designs for one-dimensional autoregressive error processes
• Optimale Zweifachblockpläne bei seriell korrelierten Fehlern
• Optimum balanced block and Latin square designs for correlated observations
• On the optimality of finite Williams II(a) designs
• On exact \(D\)-optimal designs for regression models with correlated observations
• D-optimal designs for a multivariate regression model
• When are Gauss-Markov and Least Squares Estimators Identical? A Coordinate-Free Approach
• On Canonical Forms, Non-Negative Covariance Matrices and Best and Simple Least Squares Linear Estimators in Linear Models
• Inequalities: theory of majorization and its applications
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