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A Class of Experimental Designs for Estimating a Response Surface and Variance Components
by
Liu, Hui
, Ankenman, Bruce E
, Picka, Jeffrey D
, Karr, Alan F
in
Blocking schemes
/ Civil engineering
/ Concrete
/ Degrees of freedom
/ Design of experiments
/ Estimation methods
/ Estimators
/ Experiment design
/ Factorial design
/ Factorial experiments
/ Factorials
/ Fractional factorials
/ Maximum likelihood estimation
/ Mixed effects model
/ Nested factors
/ Nesting tables
/ REML
/ Split factorial
/ Staggered nested factorial
/ Statistical variance
/ Unbiased estimators
2002
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A Class of Experimental Designs for Estimating a Response Surface and Variance Components
by
Liu, Hui
, Ankenman, Bruce E
, Picka, Jeffrey D
, Karr, Alan F
in
Blocking schemes
/ Civil engineering
/ Concrete
/ Degrees of freedom
/ Design of experiments
/ Estimation methods
/ Estimators
/ Experiment design
/ Factorial design
/ Factorial experiments
/ Factorials
/ Fractional factorials
/ Maximum likelihood estimation
/ Mixed effects model
/ Nested factors
/ Nesting tables
/ REML
/ Split factorial
/ Staggered nested factorial
/ Statistical variance
/ Unbiased estimators
2002
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Do you wish to request the book?
A Class of Experimental Designs for Estimating a Response Surface and Variance Components
by
Liu, Hui
, Ankenman, Bruce E
, Picka, Jeffrey D
, Karr, Alan F
in
Blocking schemes
/ Civil engineering
/ Concrete
/ Degrees of freedom
/ Design of experiments
/ Estimation methods
/ Estimators
/ Experiment design
/ Factorial design
/ Factorial experiments
/ Factorials
/ Fractional factorials
/ Maximum likelihood estimation
/ Mixed effects model
/ Nested factors
/ Nesting tables
/ REML
/ Split factorial
/ Staggered nested factorial
/ Statistical variance
/ Unbiased estimators
2002
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A Class of Experimental Designs for Estimating a Response Surface and Variance Components
Journal Article
A Class of Experimental Designs for Estimating a Response Surface and Variance Components
2002
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Overview
This article introduces a new class of experimental designs, called split factorials, which allow for the estimation of both response surface effects (fixed effects of crossed factors) and variance components arising from nested random effects. With an economical run size, split factorials provide flexibility in dividing the degrees of freedom among the different estimations. For a split factorial design, it is shown that the OLS estimators for the fixed effects are BLUE and that the variance component estimators from the mean squared errors on the ANOVA table are minimum variance among unbiased quadratic estimators. An application involving concrete mixing demonstrates the use of a split factorial experiment.
Publisher
Taylor & Francis,The American Society for Quality and The American Statistical Association,American Society for Quality
Subject
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