A New Approach to Optimal Design for Linear Models With Correlated Observations
From MaRDI portal
Publication:5255584
DOI10.1198/jasa.2010.tm09467zbMath1390.62151OpenAlexW2073672823MaRDI QIDQ5255584
Anatoly A. Zhigljavsky, Andrey Pepelyshev, Dette, Holger
Publication date: 16 June 2015
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: http://orca.cf.ac.uk/15206/1/ZhiglavskyDesigncorrelationsJASA.pdf
positive definite functionslogarithmic potentialssingular kernelarcsine distributionBickel-Herzberg approach
Related Items
Special issue on algorithms for design of experiments ⋮ `Nearly' universally optimal designs for models with correlated observations ⋮ Positive definiteness and the Stolarsky invariance principle ⋮ Optimal designs for regression models with autoregressive errors ⋮ Extremal measures maximizing functionals based on simplicial volumes ⋮ Optimal designs for comparing regression models with correlated observations ⋮ An extremal property of the generalized arcsine distribution ⋮ Optimal designs for the Michaelis–Menten model with correlated observations ⋮ Regression Models Augmented with Direct Stochastic Gradient Estimators ⋮ D-optimal experimental designs for a growth model applied to a Holstein-Friesian dairy farm ⋮ BLUE against OLSE in the location model: energy minimization and asymptotic considerations ⋮ Optimal design for linear models with correlated observations ⋮ Optimal designs for linear models with Fredholm-type errors ⋮ Optimal designs in regression with correlated errors ⋮ KL-optimal experimental design for discriminating between two growth models applied to a beef farm ⋮ A convex approach to optimum design of experiments with correlated observations