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Procrustes Problems

Autor: John C. Gower, Garmt B. Dijksterhuis

Limbă:

Legare: Copertă tare

Editura: Oxford University Press

Disponibilitate: În depozitul extern

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Procrustean methods are used to transform one set of data to represent another set of data as closel...

Informații despre carte

Autor

John C. Gower, Garmt B. Dijksterhuis

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2004

Pagini

248

EAN

9780198510581

ISBN

0198510586

Enbook ID

04526990

Editura

Oxford University Press

Greutate

471

Dimensiuni

161 x 242 x 18

Descriere completă

Procrustean methods are used to transform one set of data to represent another set of data as closely as possible. The name derives from the Greek myth where Procrustes invited passers-by in for a pleasant meal and a night's rest on a magical bed that would exactly fit any guest. He then either stretched the guest on the rack or cut off their legs to make them fit perfectly into the bed. Theseus turned the tables on Procrustes, fatally adjusting him to fit his own bed. This text, the first monograph on Procrustes methods, unifies several strands in the literature and contains much new material. It focuses on matching two or more configurations by using orthogonal, projection and oblique axes transformations. Group-average summaries play an important part and links with other group-average methods are discussed. This is the latest in the well-established and authoritative Oxford Statistical Science Series, which includes texts and monographs covering many topics of current research interest in pure and applied statistics. Each title has an original slant even if the material included is not specifically original. The authors are leading researchers and the topics covered will be of interest to all professional statisticians, whether they be in industry, government department or research institute. Other books in the series include 23. W.J.Krzanowski: Principles of multivariate analysis: a user's perspective updated edition 24. J.Durbin and S.J.Koopman: Time series analysis by State Space Models 25. Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger: Analysis of Longitudinal Data 2/e 26. J.K. Lindsey: Nonlinear Models in Medical Statistics 27. Peter J. Green, Nils L. Hjort & Sylvia Richardson: Highly Structured Stochastic Systems 28. Margaret S. Pepe: The Statistical Evaluation of Medical Tests for Classification and Prediction 29. Christopher G. Small and Jinfang Wang: Numerical Methods for Nonlinear Estimating Equations

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