Results 1 to 10 of about 96,969 (216)
Optimal Split-Plot Designs [PDF]
Journal of Quality Technology, 2001 Split-plot designs are heavily used in industry, especially when factor levels are difficult or costly to change or to control. This is because this type of design avoids too many changes of the whole plot factor levels, which leads to considerable savings in cost and time. The purpose of this chapter on split-plot designs is twofold.
Martina Vandebroek, Peter Goos
openaire +2 more sources
Improved Split-Plot and Multistratum Designs [PDF]
Technometrics, 2015 Many industrial experiments involve some factors whose levels are harder to set than others. The best way to deal with these is to plan the experiment carefully as a split-plot, or more generally a multistratum, design. Several different approaches for constructing split-plot type response surface designs have been proposed in the literature since 2001,
Trinca, Luzia A., Gilmour, Steven G.
openaire +4 more sources
Construction of supersaturated split-plot designs [PDF]
Statistical Papers, 2018 We propose a combinatorial construction method for setting up informative experiments with both restricted randomisation and a large number of factors. The supersaturated split-plot designs are very useful in screening situations where the number of factors is larger than the number of available observations and several of these factors have levels ...
Kalliopi Mylona +3 more
openaire +4 more sources
Split-plot designs for multistage experimentation [PDF]
Journal of Applied Statistics, 2016 ABSTRACTMost of today’s complex systems and processes involve several stages through which input or the raw material has to go before the final product is obtained. Also in many cases factors at different stages interact. Therefore, a holistic approach for experimentation that considers all stages at the same time will be more efficient. However, there
Tyssedal, John Sølve, Kulahci, Murat
openaire +3 more sources
Update formulas for split-plot and block designs [PDF]
Computational Statistics & Data Analysis, 2010 For the algorithmic construction of optimal experimental designs, it is important to be able to evaluate small modifications of given designs in terms of the optimality criteria at a low computational cost. This can be achieved by using powerful update formulas for the optimality criteria during the design construction.
ARNOUTS, Heidi, GOOS, Peter
openaire +5 more sources
Reducing the number of experiments in split-plot optimization designs [PDF]
Journal of the Brazilian Chemical Society, 2004 Two experiment reduction procedures for split-plot designs are investigated using a data set containing 160 experiments, consisting of 80 duplicate results for the optimization of a water-acetone-N,N-dimethylformamide mixture with HCl, o-dianisidine and H2O2 reagent system for the analytical determination of Cr(VI).
João Alexandre Bortoloti +3 more
openaire +5 more sources
D-optimal design of split-split-plot experiments [PDF]
Biometrika, 2009 In industrial experimentation, there is growing interest in studies that span more than one processing step. Convenience often dictates restrictions in randomization in passing from one processing step to another. When the study encompasses three processing steps, this leads to split-split-plot designs.
Bradley Jones, Peter Goos
openaire +6 more sources
Split-Plot and Multi-Stratum Designs for Statistical Inference [PDF]
Technometrics, 2017 ABSTRACTIt is increasingly recognized that many industrial and engineering experiments use split-plot or other multi-stratum structures. Much recent work has concentrated on finding optimum, or near-optimum, designs for estimating the fixed effects parameters in multi-stratum designs.
Trinca, Luzia A., Gilmour, Steven G.
openaire +5 more sources
Robust split-plot designs for model misspecification [PDF]
Journal of Quality Technology, 2018 Many existing methods for constructing optimal split-plot designs, such as D-optimal designs, only focus on minimizing the variances and covariances of the estimation for the fitted model. However, the underlying true model is usually complicated and unknown and the fitted model is often misspecified.
openaire +3 more sources
Rerandomization and Covariate Adjustment in Split-Plot Designs
Journal of Business & Economic StatisticsThe split-plot design arises from agricultural sciences with experimental units, also known as subplots, nested within groups known as whole plots. It assigns the whole-plot intervention by a cluster randomization at the whole-plot level and assigns the subplot intervention by a stratified randomization at the subplot level. The randomization mechanism
Shi, Wenqi, Zhao, Anqi, Liu, Hanzhong
openaire +2 more sources