Optimal Design of Experiments (Classics in Applied Mathematics)
โ Scribed by Friedrich Pukelsheim
- Publisher
- Society for Industrial and Applied Mathematic - SIAM
- Year
- 2006
- Tongue
- English
- Leaves
- 487
- Series
- Classics in applied mathematics volume 50
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. The optimal design for statistical experiments is first formulated as a concave matrix optimization problem. Using tools from convex analysis, the problem is solved generally for a wide class of optimality criteria such as D-, A-, or E-optimality. The book then offers a complementary approach that calls for the study of the symmetry properties of the design problem, exploiting such notions as matrix majorization and the Kiefer matrix ordering. The results are illustrated with optimal designs for polynomial fit models, Bayes designs, balanced incomplete block designs, exchangeable designs on the cube, rotatable designs on the sphere, and many other examples.
โฆ Table of Contents
Cover......Page 1
Contents......Page 8
Preface to the Classics Edition......Page 18
Preface......Page 20
ACKNOWLEDGMENTS......Page 21
List of Exhibits......Page 22
Interdependence of Chapters......Page 25
Outline of the Book......Page 26
Errata......Page 30
C H A P T E R 1
Experimental Designs in
Linear Models......Page 34
C H A P T E R 2
Optimal Designs for
Scalar Parameter Systems......Page 68
C H A P T E R 3
Information Matrices......Page 94
C H A P T E R 4
Loewner Optimality......Page 131
C H A P T E R 5
Real Optimality Criteria......Page 147
C H A P T E R 6
Matrix Means......Page 168
C H A P T E R 7
The General Equivalence
Theorem......Page 191
C H A P T E R 8
Optimal Moment Matrices
and Optimal Designs......Page 220
C H A P T E R 9
D-, A-, E-, T-Optimality......Page 243
C H A P T E R 10
Admissibility of Moment and
Information Matrices......Page 280
C H A P T E R 11
Bayes Designs and
Discrimination Designs......Page 301
C H A P T E R 12
Efficient Designs for
Finite Sample Sizes......Page 337
C H A P T E R 13
Invariant Design Problems......Page 364
C H A P T E R 14
Kiefer Optimality......Page 385
C H A P T E R 15
Rotatability and Response
Surface Designs......Page 414
Comments and References......Page 441
Biographies......Page 461
Bibliography......Page 465
Subject Index......Page 481
๐ SIMILAR VOLUMES
This is a reprint of Peter John classic book on experimental designs originally published by MacMillan but now reprinted by the Society for Industrial and Applied Mathematics in their Classics in Applied Mathematics series. The book was written in 1971 and there have been many changes in philosophy
Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. The optimal design for statistical experiments is first formulated as a concave matrix optimization problem. Using tools from convex analysis, the problem is solved generally for a wide class of opt
This book develops systematically and rigorously, yet in an expository and lively manner, the evolution of general random processes and their large time properties such as transience, recurrence, and convergence to steady states. The emphasis is on the most important classes of these processes from