TENSOR METHODS IN STATISTICS (MONOGRAPHS ON STATISTICS AND APPLIED PROBABILITY)

McCullagh, Peter

ISBN 10: 0412274809 / ISBN 13: 9780412274800

Published by Chapman & Hall Ltd, 1987

About this Item

1st 1987. Very good condition with no wrapper. A systematic development of tensor methods in statistics, beginning with the study of multivariate moments and cumulants. Dark green glazed boards with white titles. xv and 285 pages including index. Light scuffing to boards, rear boardlightly browned. Light foxing to text block. University stamp to front endpaper else contents clean. Packaged with care and promptly dispatched!

Seller Inventory # 1830325

Bibliographic Details

• Title: TENSOR METHODS IN STATISTICS (MONOGRAPHS ON STATISTICS AND APPLIED PROBABILITY)
• Publisher: Chapman & Hall Ltd
• Publication Date: 1987
• ISBN 10: 0412274809
• ISBN 13: 9780412274800
• Binding: Hardback
• Edition: First edition.
• Condition: Very Good
• Dust Jacket Condition: No Jacket

About this title

Synopsis

This book provides a systematic development of tensor methods in statistics, beginning with the study of multivariate moments and cumulants. The effect on moment arrays and on cumulant arrays of making linear or affine transformations of the variables is studied. Because of their importance in statistical theory, invariant functions of the cumulants are studied in some detail. This is followed by an examination of the effect of making a polynomial transformation of the original variables. The fundamental operation of summing over complementary set partitions is introduced at this stage. This operation shapes the notation and pervades much of the remainder of the book. The necessary lattice-theory is discussed and suitable tables of complementary set partitions are provided. Subsequent chapters deal with asymptotic approximations based on Edgeworth expansion and saddlepoint expansion. The saddlepoint expansion is introduced via the Legendre transformation of the cumulant generating function, also known as the conjugate function of the cumulant generating function. A recurring them is that, with suitably chosen notation, multivariate calculations are often simpler and more transparent than the corresponding univariate calculations. The final two chapters deal with likelihood ratio statistics, maximum likelihood estimation and the effect on inferences of conditioning on ancillary or approximately ancillary statistics. The Bartlett adjustment factor is derived in the general case and simplified for certain types of generalized linear models. Finally, Barndorff-Nielsen's formula for the conditional distribution of the maximum liklelihood estimator is derived and discussed. More than 200 Exercises are provided to illustrate the uses of tensor methodology.