Response Surface Models with Random Block Effects
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Publication:4029696
DOI10.2307/1269549zbMath0850.62618OpenAlexW2136029329MaRDI QIDQ4029696
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Publication date: 1 April 1993
Published in: Technometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2307/1269549
Related Items (22)
Estimating the Intercept in an Orthogonally Blocked Experiment when the Block Effects are Random ⋮ A graphical method for evaluating the effect of blocking in response surface designs ⋮ PREDICTION OF THE VARIATIONS OF THE MEAN RESPONSE BY USING EXPERIMENTAL DESIGN WITH QUANTITATIVE FACTORS AND RANDOM BLOCK EFFECTS ⋮ Prediction based on response surface data obtained with random blocking ⋮ Interblock information in multidimensional block designs ⋮ Robust population designs for longitudinal linear regression model with a random intercept ⋮ Some practical advice on polynomial regression analysis from blocked response surface designs ⋮ An algorithmic framework for generating optimal two-stratum experimental designs ⋮ \(\mathcal{D}\)-optimal minimum support mixture designs in blocks ⋮ D-optimal two-level parallel-flats block designs with partial replication ⋮ A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity ⋮ On the existence of the Gauss-Markov estimators in linear mixed models ⋮ On the analysis of paired observations. ⋮ \(V\)- and \(D\)-optimal population designs for the simple linear regression model with a random intercept term ⋮ Optimal \(s^n\) factorial designs when observations within-blocks are correlated ⋮ \(D\)-optimal response surface designs in the presence of random block effects. ⋮ Blocking response surface designs ⋮ \(D\)-optimal design for a model with interaction between a qualitative and a quantitative factor in the presence of random block effects ⋮ Multiresponse surface models with random block effects. ⋮ Multiresponse surface models with block effects ⋮ Optimal versus orthogonal and equivalent-estimation design of blocked and split-plot experiments ⋮ On choosing between fixed and random block effects in some no-interaction models
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