Statistical Inference in Second Order RSM Optimization

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 105)

We continue in this chapter the discussion of methods for dealing with sampling variability in experimental optimization techniques. This chapter considers the effect of statistical sampling error in RSM techniques that are based on second order (quadratic) polynomial models. We first discuss finding confidence intervals for the eigenvalues of the Hessian matrix, that is, the effect of sampling variability in canonical analysis. Later sections consider the related and important problem of finding a confidence region for the optimal operating conditions x0. The unconstrained case is discussed first after which methods for the computation and display of confidence regions on constrained optima are discussed. Any traditional (frequentist) RSM optimization analysis should probably always include such regions.


Stationary Point Response Surface Model Sampling Variability Curly Brace Full Quadratic Model 
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© Springer Science+Business Media, LLC 2007

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