Functional Approach to Optimal Experimental Design / Lecture Notes in Statistics Bd.184 (PDF)
The book presents a novel approach for studying optimal experimental designs. The functional approach consists of representing support points of the designs by Taylor series. It is thoroughly explained for many linear and nonlinear regression models...
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The book presents a novel approach for studying optimal experimental designs. The functional approach consists of representing support points of the designs by Taylor series. It is thoroughly explained for many linear and nonlinear regression models popular in practice including polynomial, trigonometrical, rational, and exponential models. Using the tables of coefficients of these series included in the book, a reader can construct optimal designs for specific models by hand.
The book is suitable for researchers in statistics and especially in experimental design theory as well as to students and practitioners with a good mathematical background.
Viatcheslav B. Melas is Professor of Statistics and Numerical Analysis at the St. Petersburg State University and the author of more than one hundred scientific articles and four books. He is an Associate Editor of the Journal of Statistical Planning and Inference and Co-Chair of the organizing committee of the 1st-5th St. Petersburg Workshops on Simulation (1994, 1996, 1998, 2001 and 2005).
- Autor: Viatcheslav B. Melas
- 2006, 2006, 338 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 0387316108
- ISBN-13: 9780387316109
- Erscheinungsdatum: 20.04.2006
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- Größe: 2.92 MB
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From the reviews:
"This monograph is a welcome addition to the statistical literature on optimal experimental designs. … This will be a useful reference book for researchers in this area." (Damaraju Raghavarao, Mathematical Reviews, Issue 2006 k)
"The material presented is at a sophisticated mathematical level, with a strong emphasis on the construction to why these strategies create designs that are desirable for practical implementation. The unified approach helps consolidate the individual pieces that the author has published previously, and provides greater insights into the applicability of the methods." (Christine M. Anderson-Cook, Journal of the American Statistical Association, Vol. 102, No. 477, 2007)
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