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Showing 1 to 20 of 28 for “"D-Optimal"”.
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Bayesian D-Optimal Design for Generalized Linear Models
Bayesian optimal designs have received increasing attention in recent years, especially in biomedical and clinical trials. Bayesian design procedures can utilize the available prior information of the unknown parameters so that a better design can be achieved. However, a difficulty in dealing with …
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Metamodeling utilizing d-optimal designs as applied to large scale simulations
Includes bibliographical references (pages 127-130).
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One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models
In this research, Bayesian two-stage D-D optimal designs for mixture experiments with or without process variables under model uncertainty are developed. A Bayesian optimality criterion is used in the first stage to minimize the determinant of the posterior variances of the parameters. The second …
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ΕΙΔΙΚΕΣ ΚΑΤΑΣΚΕΥΕΣ ΒΕΛΤΙΣΤΩΝ ΠΕΙΡΑΜΑΤΙΚΩΝ ΣΧΕΔΙΑΣΜΩΝ
… N=40, 44, 48, 52, 80, 84. THESE GIVE "ALMOST" D-OPTIMAL SATURATED DESIGNS WHEN THE NUMBER OF OBSERVATIONS IS = 1 MOD 4. ALGORITHMS ARE GIVEN WHICH ARE COMPUTER IMPLEMENTED. CHAP. 2ND: A NEW METHOD IS GIVEN FOR CONSTRUCTING D-OPTIMAL WEIGHING DESIGNS WHEN N=3 MOD 4. A MATRIX OF THE GOETHALS-SEIDEL …
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Optimal Experimental Designs for the Poisson Regression Model in Toxicity Studies
Optimal experimental designs for generalized linear models have received increasing attention in recent years. Yet, most of the current research focuses on binary data models especially the one-variable first-order logistic regression model. This research extends this topic to count data models. …
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Optimal and sequential design for bridge regression with application in organic chemistry
… method is developed for the selection of an optimal design when accurate estimates of the model coefficients are required. The method exploits a relationship between bridge regression and Bayesian methods which is used to develop a class of D-optimal designs. A necessary approximation to the …
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Statistical Methods For Kinetic Modeling Of Fischer Tropsch Synthesis On A Supported Iron Catalyst
… from a proposed FTS mechanism, was used with D-optimal criterion to develop experiments sequentially at 220°C and 239°C. Joint likelihood confidence regions for the rate expression parameters with respect to run number indicate rapid convergence to precise-parameter estimates. Difficulty …
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Optimality criteria applied to certain response surface designs
… primary goal. A design criterion, D<sub>S</sub>-optimality, was applied to several classes of RSM designs to find optimal choices of design parameters. Further, previous results on D-optimal RSM designs were extended. The designs resulting from the use of the two criteria were compared. Two other …
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A two-stage experimental design procedure under dispersion effects
Under heterogeneous variance, conventional optimal response surface experimental designs for estimating location models are no longer optimal. To address this deficiency. D and Q criteria appropriate under heterogeneous variance are developed. These criteria are then applied to demonstrate the …
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Optimal experimental designs for two-variable logistic regression models
… becomes more complex and expensive. The optimal design work is extremely valuable in areas such as biomedical and environmental applications. Most design research dealing with the logistic model has been concentrated on the one-variable case. Relative little has been done for the …
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New Theory and Algorithms for Convex Optimization with Non-Standard Structures
… solution, where 𝛿0 denotes the initial optimality gap and 𝑅ℎ is the variation of ℎ on its domain. This result establishes certain intrinsic connections between 𝜃-logarithmically homogeneous barriers and the Frank-Wolfe method. When specialized to the 𝐷-optimal design problem, we …
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Optimal designs for a bivariate logistic regression model
… model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In …
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Measurement Error in Designed Experiments for Second Order Models
… order models. The results are used to suggest optimal values for axial points in Central Composite Designs. The proper analysis for experimental data including ME is outlined for first and second order models. A comparison of this analysis to a typical Ordinary Least Squares analysis is made …
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Optimal designs for dose-finding in contingent response models
We study D- and c-optimal designs for dose-finding with opposing failure functions. In particular, we study the contingent response models of Li, Durham and Flournoy (1995). In the contingent response model, there are two opposing types of failure. We call one failure type toxicity and the other …
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Response surface designs for the detection of model inadequacy
… we obtain several results for rotations of D-optimal and Λ(T)-optimal designs. Optimal designs for all of these criteria are obtained and evaluated for a variety of cases. Primary consideration is given to the use of τ₁ and τ₂ for one and two factor, first-order vs. second-order and …
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Response Surface Design and Analysis in the Presence of Restricted Randomization
… of numerical verification in generating D-optimal and minimal point designs, including split-plot versions of the Notz, Hoke, Box and Draper, and hybrid designs. Finally, we consider the practical implications of analyzing a near-equivalent design when a suitable equivalent design is not …
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Parameter Identification and the Design of Experiments for Continuous Non-Linear Dynamical Systems
Mathematical models are useful for simulation, design, analysis, control, and optimization of complex systems. One important step necessary to create an effective model is designing an experiment from which the unknown model parameter can be accurately identified and then verified. The strategy …
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Likelihood Theory and Methods for Generalized Linear Mixed Models
… constructing studentized confidence intervals or optimal design determination. In this thesis, we derive precise asymptotic results for likelihood-based generalized linear mixed model analysis. The novel asymptotic normality results are derived for both cases involving either a canonical or …
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The performance of orthogonal arrays with adjoined or unavailable runs
… a class of fractional factorial designs that are optimal according to a range of optimality criteria. This makes it tempting to construct fractional factorial designs by adjoining additional runs to an OA, or by removing runs from an OA, when the number of runs available for the experiment is only …
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Consumer Choice of Hotel Experiences: The Effects of Cognitive, Affective, and Sensory Attributes
… be used for the choice modeling and to create an optimal choice design. I used a Bayesian D-optimal design for the choice experiment, which I assess from the DOE (design of experiment) procedure outlined in JMP 8.0. The primary analysis associated with discrete choice analysis is the …
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