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Showing 1 to 12 of 12 for “"response surface designs"”.
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Outliers and robust response surface designs
A commonly occurring problem in response surface methodology is that of inconsistencies in the response variable. These inconsistencies, or maverick observations, are referred to here as outliers. Many models exist for describing these outliers. Two of these models, the mean shift and the variance …
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Optimality criteria applied to certain response surface designs
… polynomial model was shown to be important in Response Surface Methodology (RSM). This led naturally to designing RSM experiments for best estimation of these coefficients as a primary goal. A design criterion, D<sub>S</sub>-optimality, was applied to several classes of RSM designs to find …
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Response surface designs for the detection of model inadequacy
… it is tentatively assumed that the experimental response is related to some independent variables, x, by η₁(x) = x₁’ β₁. However, there is frequently some doubt whether this model adequately approximates the true response function, so a lack of fit test is used as part of the analysis. We suppose …
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Graphical assessment of the prediction capability of response surface designs
A response surface analysis is concerned with the exploration of a system in order to determine the behavior of the response of the system as levels of certain factors which influence the response are changed. It is often of particular interest to predict the response in some region of the …
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Response surface designs and analysis for bi-randomization error structures
… randomizations may dictate the necessity to run response surface experiments in a bi-randomization error control format of which the split plot design is a special case. A bi-randomization scheme allows for certain factor levels to be applied at random to large experimental units with the …
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A graphical approach for evaluating the potential impact of bias due to model misspecification in response surface designs
The basic purpose of response surface analysis is to generate a relatively simple model to serve as an adequate approximation for a more complex phenomenon. This model then may be used for other purposes, for example prediction or optimization. Since the proposed model is only an approximation, the …
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Understanding Scaled Prediction Variance Using Graphical Methods for Model Robustness, Measurement Error and Generalized Linear Models for Response Surface Designs
… are becoming important tools for evaluating designs. The need to compare designs in term of their prediction variance properties advanced this development. A recent graphical tool, the Fraction of Design Space plot, is useful to calculate the fraction of the design space where the scaled …
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Bayesian hierarchical modelling of dual response surfaces
Dual response surface methodology (Vining and Myers (1990)) has been successfully used as a cost-effective approach to improve the quality of products and processes since Taguchi (Tauchi (1985)) introduced the idea of robust parameter design on the quality improvement in the United States in …
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Neural networks as a tool for statistical modeling
… the SHLFNN is being used more frequently is in response surface modeling based on designed experiments. Due to the small sample sizes typically employed by response surface designs, the ability of the SHLFNN to accurately approximate the underlying model is questionable. The results of a …
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Recommendations for Design Parameters for Central Composite Designs with Restricted Randomization
In response surface methodology, the central composite design is the most popular choice for fitting a second order model. The choice of the distance for the axial runs, alpha, in a central composite design is very crucial to the performance of the design. In the literature, there are plenty of …
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A response surface approach to the mixture problem when the mixture components are categorized
… purpose of this paper is to show how standard response surface designs and polynomial models can be used for estimating the response to mixtures of the k mixture components. The experimentation is concentrated in an ellipsoidal region chosen by the experimenter, subject to the constraints …