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Arnold, a member of the Hodder Headline group, Publisher of Kendall's Library of Statistics.

Kendall’s Advanced Theory of Statistics, Volume 2A: Classical Inference and the Linear Model, 6th edition

The late Alan Stuart, Former Professor Emeritus of Statistics, London School of Economics, UK
J. Keith Ord, Professor of Decision Sciences, Georgetown University, Washington DC, USA
Steven Arnold, Professor of Statistics, The Pennsylvania State University, USA.

Kendall’s Advanced Theory of Statistics, Volume 2A

Published: 1998, Hardback, 912pp, ISBN: 0 340 662301, Price: £90.00

Reviews:

'The general authority of the work, together with its lasting value as a reference source, make it the Bible which no statistician should be without.' The Statistician

'As a comprehensive reference...it is unsurpassed.' International Statistics Review

'A book of outstanding importance which surveys and integrates an immense field of knowledge.' Journal of the Royal Statistical Society

'Will occupy a central place in the statistical literature.' Nature

'Monumental in size, organisation and technical competence...ownership of this book is a professional necessity.' Aslib Book List

Key Features:

  • All recent and important statistical developments in one volume
  • New chapter on the linear model and least squares estimation
  • Cross-referenced to the other volumes in Kendall’s Advanced Theory of Statistics.

Description:

Covering the classical theory of inference and the linear model, this sixth edition of 'Kendall's Advanced Theory of Statistics' covers estimation theory, interval estimation, the testing of hypothetical regression and comparative inference. Statistical theory has moved on considerably since the last edition and the authors have made sure that all the newest developments are included. In particular there is a new chapter on the linear model and least squares estimation.

Readership:

All statisticians at all levels.

Contents:

Preface to the sixth edition
List of examples
Glossary of abbreviations
17. Estimation and sufficiency
18. Estimation: maximum likelihood and other methods
19. Interval estimation
20. Tests of hypotheses: simple null hypotheses
21. Tests of hypotheses: composite hypotheses
22. Likelihood ratio tests and test efficiency
23. Invariance and equivariance
24. Sequential methods
25. Tests of fit
26. Comparative statistical inference
27. Statistical relationship linear regression and correlation
28. Partial and multiple correlation
29. The general linear model
30. Fixed effects analysis of variance
31. Other analysis of variance models
32. Analysis and diagnosis for the linear model
Appendix tables
References
Index of examples in text
Author index
Subject index

 

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