Text and Readings in Mathematics/ 45
February 2007
168 pages
Hardcover
ISBN 81-85931-75-5
The aim of this little book is to convey three principal
developments in the evolution of modern information theory:
Shannon's initiation of a revolution in 1948 by his
interpretation of Boltzmann entropy as a measure of information
yielded by an elementary statistical experiment and basic coding
theorems on storing messages and transmitting them through noisy
communication channels in an optimal manner; the influence of
ergodic theory in the enlargement of the scope of Shannon's
theorems through the works of McMillan, Feinstein, Wolfowitz,
Breiman and others and its impact on the appearance of the
Kolmogorov-Sinai invariant for elementary dynamical systems; and
finally, the more recent work of Schumacher, Holevo, Winter and
others on the role of von Neumann entropy in the quantum avatar
of the basic coding theorems when messages are encoded as quantum
states, transmitted through noisy quantum channels and retrieved
by generalized measurements.
Contents
1 Entropy of Elementary Information Sources
2 Stationary Information Sources
3 Communication in the Presence of Noise
4 Quantum Coding Theorems
Bibliography
Index
ISBN: 978-0-471-22618-5
Hardcover
400 pages
March 2007
The first edition of this text has sold over 19,600 copies.
However, the use of statistical methods for categorical data has
increased dramatically in recent years, particularly for
applications in the biomedical and social sciences. A second
edition of the introductory version of the book will suit it
nicely. Wiley also published a second edition of Categorical Data
Analysis, which is an advanced, more technical text, in 2003.
Table of contents
1. Introduction.
2. Contingency Tables.
3. Generalized Linear Models.
4. Logistic Regression.
5. Building and Applying Logistic Regression Models.
6. Multicategory Logit Models.
7. Loglinear Models for Contingency Tables.
8. Models for Matched Pairs.
9. Modelling Correlated, Clustered Responses.
10. Random Effects: Generaizaed Linear Mixed Models.
11. A Historical Tour of Cataegorical Data Analysis.
Appendix: Software for Categorical Data Analysis.
Table of Chi-Squared Distribution Values.
Bibliography.
Index of Examples.
Subject Index.
Answers to Selected Odd-Numbered Exercises.
ISBN: 978-0-470-51024-7
Hardcover
960 pages
July 2007
The R language is recognized as one of the most powerful and
flexible statistical software packages, and it enables the user
to apply many statistical techniques that would be impossible
without such software to help implement such large data sets. R
is becoming evermore essential both to carry out research and to
understand it, as more and more people present their results in
the context of R.
The R Book introduces the advantages of the R environment, in a
user-friendly format, to beginners and intermediate users in a
range of disciplines, from science and engineering to medicine
and economics. The format enables it to be either read as a text,
or dipped-into as a reference manual.
The early chapters assume no background in statistics or
computing, and introduce the reader to the basic concepts
involved. In this way the reader is introduced to the assumptions
that lie behind the tests, fostering a critical approach to
statistical modeling. Subsequent chapters examine more advanced
topics, cementing what is learnt in the opening chapters, as well
as benefiting more intermediate readers. Throughout the book, the
readerfs experience is furthered by practical guidance and the
inclusion of numerous worked examples.
ISBN: 978-0-470-11402-5
Hardcover
304 pages
March 2007
During the last decades long-memory processes have evolved as a
vital and important part of time series analysis. This book
attempts to give an overview of the theory and methods developed
to deal with long-range dependent data as well as describe some
applications of these methodologies to real-life time series. The
topics are systematically organized in a progressive manner,
starting from foundations (the first three chapters), progressing
to the analysis of methodological implications (the next six
chapters), and finally extending to more complex long-range
dependent data structures (the final three chapters).
Table of contents
Preface.
Acronyms.
1. Stationary Processes.
2. State Space Systems.
3. Long-Memory Processes.
4. Estimation Methods.
5. Asymptotic Theory.
6. Heteroskedastic Models.
7. Transformations.
8. Bayesian Methods.
9. Prediction.
10. Regression.
11. Missing Data.
12. Seasonality.
References.
Topic Index.
Author Index.
Paperback (ISBN-13: 9780521705189)
This definitive introduction to finite element methods has been thoroughly updated for a third edition which features important new material for both research and application of the finite element method. The discussion of saddle-point problems is a highlight of the book and has been elaborated to include many more nonstandard applications. The chapter on applications in elasticity now contains a complete discussion of locking phenomena. The numerical solution of elliptic partial differential equations is an important application of finite elements and the author discusses this subject comprehensively. These equations are treated as variational problems for which the Sobolev spaces are the right framework. Graduate students who do not necessarily have any particular background in differential equations, but require an introduction to finite element methods will find this text invaluable. Specifically, the chapter on finite elements in solid mechanics provides a bridge between mathematics and engineering.
* Extra material that will be useful for both research and applications
* Chapter specifically aimed at engineering applications gives wide appeal; chapter on saddle-point problems has been elaborated and developed for this edition
* Ideal as a graduate level introduction to this important field
Contents
Preface to the Third English Edition; Preface to the First English Edition; Preface to the German Edition; Notation; 1. Introduction; 2. Conforming finite elements; 3. Nonconforming and other methods; 4. The conjugate gradient method; 5. Multigrid methods; 6. Finite elements in solid mechanics; References; Index.