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Optimal Design of Experiments
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Experimental designs in linear models Optimal designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of moment and information matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index as discussed by the authorsAbstract:
Experimental Designs in Linear Models Optimal Designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of Moment and Information Matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index.read more
Citations
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Bayesian Experimental Design: A Review
TL;DR: This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models, and presents a uniied view of the topic by putting experimental design in a decision theoretic framework.
Journal ArticleDOI
Sensor Selection via Convex Optimization
Siddharth Joshi,Stephen Boyd +1 more
TL;DR: This paper describes a heuristic, based on convex optimization, that gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements.
Journal ArticleDOI
Making and Evaluating Point Forecasts
TL;DR: In this paper, the authors demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are carefully matched, and demonstrate that point forecasting methods are compared by means of an error measure or scoring function, with the absolute error and the squared error being key examples.
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Uniform Design: Theory and Application
TL;DR: It is shown that UD's have many desirable properties for a wide variety of applications and the global optimization algorithm, threshold accepting, is used to generate UD's with low discrepancy.
Journal Article
Covariate Shift Adaptation by Importance Weighted Cross Validation
TL;DR: This paper proposes a new method called importance weighted cross validation (IWCV), for which its unbiasedness even under the covariate shift is proved, and the IWCV procedure is the only one that can be applied for unbiased classification under covariates.
References
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Book
Optimum experimental designs
Anthony C. Atkinson,A. N. Donev +1 more
TL;DR: In this article, the authors present an analysis of experiments with both qualitative and quantitative factors: Blocking response surface designs, restricted region designs, failure of the experiment and design augmentation, and discrimination between models.
Journal ArticleDOI
The Equivalence of Two Extremum Problems
J. Kiefer,J. Wolfowitz +1 more
TL;DR: In this article, the authors consider the problem of defining probability measures with finite support, i.e., measures that assign probability one to a set consisting of a finite number of points.