
Model-Oriented Design of Experiments
Springer (Publisher)
Published on 20. June 1997
Book
Paperback/Softback
VIII, 132 pages
978-0-387-98215-1 (ISBN)
Article exhausted; check for reprint
Description
These lecture notes are based on the theory of experimental design for courses given by Valerii Fedorov at a number of places, most recently at the University of Minnesota, the Vienna of University, and the University of Economics and Business Administra tion in Vienna. It was Peter Hackl's idea to publish these lecture notes and he took the lead in preparing and developing the text. The work continued longer than we expected, and we realized that a few thousand miles distance remains a serious hurdle even in the age of Internet and many electronic gadgets. While we mainly target graduate students in statistics, the book demands only a moderate background in calculus, matrix algebra and statistics. These are, to our knowledge, provided by almost any school in business and economics, natural sciences, or engineering. Therefore, we hope that the material may be easily understood by a relatively broad readership. The book does not try to teach recipes for the construction of experimental de signs. It rather aims at creating some understanding - and interest - in the problems and basic ideas of the theory of experimental design. Over the years, quite a number of books have been published on that subject with a varying degree of specialization. This book is organized in four chapters that layout in a rather compact form all.
More details
Series
Edition
1997 ed.
Language
English
Place of publication
NY
United States
Target group
College/higher education
Professional and scholarly
Research
Illustrations
VIII, 132 p.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
454 gr
ISBN-13
978-0-387-98215-1 (9780387982151)
DOI
10.1007/978-1-4612-0703-0
Schweitzer Classification
Other editions
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Valerii V. Fedorov | Peter Hackl
Model-Oriented Design of Experiments
Book
12/2024
2nd Edition
Springer
€106.99
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Content
1 Some Facts From Regression Analysis.- 1.1 The Linear Model.- 1.2 More about the Information Matrix.- 1.3 Generalized Versions of the Linear Regression Model.- 1.4 Nonlinear Models.- 2 Convex Design Theory.- 2.1 Optimality Criteria.- 2.2 Some Properties of Optimality Criteria.- 2.3 Continuous Optimal Designs.- 2.4 The Sensitivity Function and Equivalence Theorems.- 2.5 Some Examples.- 2.6 Complements.- 3 Numerical Techniques.- 3.1 First Order Algorithm:D-criterion.- 3.2 First Order Algorithm: The General Case.- 3.3 Finite Sample Size.- 4 Optimal Design under Constraints.- 4.1 Cost Constraints.- 4.2 Constraints for Auxiliary Criteria.- 4.3 Directly Constrained Design Measures.- 5 Special Cases and Applications.- 5.1 Designs for Time-Dependent Models.- 5.2 Regression Models with Random Parameters.- 5.3 Mixed Models and Correlated Observations.- 5.4 Design for "Contaminated" Models.- 5.5 Model Discrimination.- 5.6 Nonlinear Regression.- 5.7 Design in Functional. Spaces.- A Some Results from Matrix Algebra.- B List of Symbols.- References.