Some practical advice on polynomial regression analysis from blocked response surface designs

DOI10.1080/03610920008832601zbMath1061.62550OpenAlexW1976961995WikidataQ60567950 ScholiaQ60567950MaRDI QIDQ4550651

Steven G. Gilmour, Luzia A. Trinca

Publication date: 2000

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920008832601

zbMATH Keywords

industrial experimentssedond-order model

Mathematics Subject Classification ID

Statistical block designs (62K10) Response surface designs (62K20)

Related Items (9)

Estimating the Intercept in an Orthogonally Blocked Experiment when the Block Effects are Random ⋮ An algorithmic framework for generating optimal two-stratum experimental designs ⋮ A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity ⋮ A multi-objective coordinate-exchange two-phase local search algorithm for multi-stratum experiments ⋮ \(D\)-optimal response surface designs in the presence of random block effects. ⋮ Blocking response surface designs ⋮ Optimal designs for conjoint experiments ⋮ Response Surface Designs for Experiments in Bioprocessing ⋮ \(D\)-optimal design for a model with interaction between a qualitative and a quantitative factor in the presence of random block effects

Uses Software

• GenStat

Cites Work

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• Approximations for Standard Errors of Estimators of Fixed and Random Effect in Mixed Linear Models
• Computer-Aided Blocking of Factorial and Response-Surface Designs
• A Unified Approach to Design of Experiments
• Unbiasedness of two-stage estimation and prediction procedures for mixed linear models
• Response Surface Models with Random Block Effects
• Applied regression analysis bibliography update 1994-97
• Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models
• Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
• Difference Variance Dispersion Graphs for Comparing Response Surface Designs with Applications in Food Technology

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