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Nonparametric Bootstrap Inference on the Characterization of a Response Surface Robert Parody Center for Quality and Applied Statistics Rochester Institute of Technology 2009 QPRC June 4, 2009

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Presentation Outline Introduction Previous Work New Technique Example Simulation Study Conclusion and Future Research

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Introduction Response Surface Methodology (RSM) –Identify the relationship between a set of k- predictor variables and the response variable y –Typically, the goal of the experiment is to optimize E(Y) is transformed into coded x by is transformed into coded x by

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The Model A second order model is fit to the data represented by –where: i, ii, and ij are unknown parameters i, ii, and ij are unknown parameters ~ F(0, 2 ) and independent ~ F(0, 2 ) and independent u are other effects such as block effects and covariates, which are not interacting with the x i s u are other effects such as block effects and covariates, which are not interacting with the x i s

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Equivalently, in matrix form,

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Background Canonical Analysis Rotate the axis system so that the new system lies parallel to the principle axes of the surface P is the matrix of eigenvectors of B where P P = PP = I The rotated variables and parameters: – – w = P x – – = P – – = P BP = diag( i )

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If all i 0), the stationary point is a maximizer (minimizer); contours are ellipsoidal. If the i have different signs, the stationary point is a minimax point (complicated hyperbolic contours). Types of Surfaces

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Standard Errors for the i Carter Chinchilli and Campbell (1990) –Found standard errors and covariances for i by way of the delta method Bisgaard and Ankenman (1996) –Simplified this with the creation of the Double Linear Regression (DLR) method

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Previous Work Edwards and Berry (1987) –Simulated a critical point for a prespecified linear combination of the parameters –The natural pivotal quantity for constructing simultaneous intervals for these linear combinations of the parameters is

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Shortcoming The technique on the previous slide is only valid when The technique on the previous slide is only valid when –The errors are i.i.d. normal with constant variance –The set of linear combinations of interest are prespecified

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Research Goal Employ a nonparametric bootstrap based on a pivotal quantity to extend the previously mentioned work to include situations where: Employ a nonparametric bootstrap based on a pivotal quantity to extend the previously mentioned work to include situations where: 1. The set of linear combinations of interest are not prespecified 2. Relax the error distribution assumption

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12 Bootstrap Idea Resample from the original data – either directly or via a fitted model – to create replicate datasets Use these replicate datasets to create distributions for parameters of interest Consider the nonparametric version by utilizing the empirical distribution

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13 Empirical Distribution The empirical distribution is one which equal probability 1/N is given to each sample value y i The corresponding estimate of the cdf F is the empirical distribution function (EDF), which is defined as the sample proportion:

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New Technique The pivotal quantity for simultaneous inference on i :

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Bootstrap Equivalent Replace the parameter with the estimates and the estimates with the bootstrap estimates to get:

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Bootstrap Parameter Estimation Find the model fits Resample from the modified residuals N times with replacement Add these values to the fits and use them as observations Fit the new model and determine the bootstrap parameter estimates

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17 An Adjustment We usually at least assume that the errors are iid from a distribution with mean 0 and constant variance 2 The residuals on the other hand come from a common distribution with mean 0 and variance 2 (1-h ii ) So the modified residuals become

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Critical Point Procedure Create nonparametric bootstrap estimates for the unknown parameters in Q* Now find Q* by maximizing over the j elements Repeat this process for a large number of bootstrap samples (m) and take the (m+1)(1- ) th order statistic

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Bootstrap Simulation Size Edwards and Berry (1987) showed conditional coverage probability of 95% simulation-based bounds will be +/-0.002 for 99% of the generations for (m+1)=80000

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Example Chemical process experiment with k=5 from Box (1954) Goal: Maximize percentage yield

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Parameter Estimates ParameterEstimate 1 -0.041 2 -0.400 3 -1.782 4 -2.625 5 -4.461

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Parameter Estimates ParameterEstimate 1 -0.041 2 -0.400 3 -1.782 4 -2.625 5 -4.461

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Critical Point Using =0.05 and (m+1)=80000, we get

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Estimates and Estimates and 95% Simultaneous Confidence Intervals ParameterLCLEstimateUCL 1 -0.741-0.0410.660 2 -0.840-0.4000.045 3 -2.553-1.782-1.011 4 -3.332-2.625-1.918 5 -5.205-4.461-3.717

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Estimates and Estimates and 95% Simultaneous Confidence Intervals ParameterLCLEstimateUCL 1 -0.741-0.0410.660 2 -0.840-0.4000.045 3 -2.553-1.782-1.011 4 -3.332-2.625-1.918 5 -5.205-4.461-3.717

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Comparison of critical points –For the example, we would only need ~88% of the sample size for the simulation method as compared to traditional simultaneous methods Computer Time –Approximately 2 minutes on a Intel Core 2 Duo computer Relative Efficiency

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Simulation Study 10 critical points were created For each critical point, 10000 confidence intervals were created by bootstrapping the residuals This was done 100 times for each point

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Simulation Results

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Conclusions New technique yields tighter bounds Works for linear combinations not prespecified Relaxes normality assumption on the error terms Simulation study yields adequate coverage

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Future Research Relax model assumptions further to include nonhomogeneous error variances Apply to other situations where we are unable to prespecify the combinations, such as ridge analysis

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