Applied Optimal DesignsThere is an increasing need to rein in the cost of scientific study without sacrificing accuracy in statistical inference. Optimal design is the judicious allocation of resources to achieve the objectives of studies using minimal cost via careful statistical planning. Researchers and practitioners in various fields of applied science are now beginning to recognize the advantages and potential of optimal experimental design. Applied Optimal Designs is the first book to catalogue the application of optimal design to real problems, documenting its widespread use across disciplines as diverse as drug development, education and ground water modelling. Includes contributions covering:
Applied Optimal Designs bridges the gap between theory and practice, drawing together a selection of incisive articles from reputed collaborators. Broad in scope and inter-disciplinary in appeal, this book highlights the variety of opportunities available through the use of optimal design. The wide range of applications presented here should appeal to statisticians working with optimal designs, and to practitioners new to the theory and concepts involved. |
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Contents
Optimal Design in Educational Testing | 1 |
Steven Buyske Department of Mathematics | 13 |
Acknowledgements | 16 |
Copyright | |
10 other sections not shown
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Common terms and phrases
algorithm application of optimal Applied Psychological Measurement approach arterial assume Berger block effects model blocked experiment CBFmax coefficient computed conjoint analysis constraints construction contamination cost covariance matrix criteria criterion curve D-optimal denote design for estimating design of experiments design points design problem dichotomous dose efficiency equation example factor Fedorov Figure first-stage Fisher information Fisher information matrix groundwater Hellinger distances Hill equation information matrix item parameters item response theory kinetics least squares estimator levels linear locally optimal designs logistic regression mathematical Meanscore method microbial growth microbiology minimizes model parameters Monod model nonlinear regression number of observations obtained optimal design theory optimal experimental design optimal sampling fraction outcome variable paired comparisons parameter estimation pilot data plume random block effects respect run orders S-PLUS sampling fractions Section selected simulation specific growth rate Statistics strata Table test-takers testlets trend unknown parameters utilities variance variance-covariance matrix vector venous weight Wong
References to this book
Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |