This Reza book is what the Ash book should have been (both are published by Dover). While the Ash book focuses on much more
advanced topics in information theory and requires a much higher level knowledge of mathematics, it gives no real clue
whatsoever as to what information theory really is... at least
if your of the "engineer" mentality... the Ash book won't be
much help because it's to rigorous and not practical enough
(i.e. what's the relavance of information theory to communications). In comes the Reza book. This is truly one of the finest books for beginners in information theory. It covers very extensively the basics of "classic information theory," although it's shortcoming is that like the Ash book, the perspective of the book is more mathematical and it really doesnt emphasize enough the "practical" applications of information theory as it relates to electronic communications.

Reza is an outstanding author in that he can explain things in words, and doesn't need to bedazzle you with mathematical equations.. Reza makes you understand what it's all about.
If your seeking something even simpler... only one book fits the bill... I highly recommend Pierce's information theory book, subtitled "symbols, signals, and noise" (also by Dover) which is probably THE BEST/SIMPLEST INFORMATION THEORY BOOK EVER WRITTEN. Way too simplistic in my opinion... but it's great light reading, because Pierce is also a fabulous author, one of the best of his era, and for those not math/science/engineering oriented, it would be a great
book to read.
Summary, buy this book before you buy the Ash book.

This book is the best, like introduction in the theory
information. The examples are great, the analogies with
the circuit are helpful. The review of mathematical backgrounds,
in special the statistical, the theorems, permit a
good comprehension.
The new books in this topic are bad, because they separate
the aplications and the theory, don't waste your money in them!

Like most Dover mathematics books, this is a high-quality reprint of an older textbook (1961). I have read the first 100 pages, and am very impressed thus far. The writing is crisp and clear, and moves at a good pace. The book seems to me to be ideal for self-study and as a lead-in to more modern (and complex) treatments. In a way, the age of the book stands in its favor, as Information Theory was just being canonized at about the time of publication. Thus, this book attempts to organize and present about 20 years worth of research, rather than the 60 or so years that modern authors may feel compelled to include. Thank God for Dover books.

This book provides a good coverage of the very useful bootstrap method. However, post-graduation as an engineer, I find that the method is neither well known nor happily accepted by engineers outside of academia. In the corporate world, bootstrapping is left up to degreed statisticians, as this is what management trusts. As a mechanical engineer, I find that simpler statistical techniques, even if they include broad assumptions, are much more widely accepted. If you are an engineer, leave this up to the statisticians. If you are a statistician, this book is an acceptable source for learning bootstrap.

Brad Efron wrote the key paper rediscovering the bootstrap and putting it in its proper place with other resampling techniques in his famous 1979 paper in the Annals of Statistics. His work was a breakthrough that has now led to hundreds of other publications and several books on the bootstrap and more general resampling procedures by himself, his students and many other statisticians. In fact I am working on a book with goals similar to what he and Rob Tibshirani achieve in this monograph. It is a concise and accurate presentation of the bootstrap and its wide variety of applications and is very much up to the state-of-the-art in this rapidly growing area of statistics. It is written in an intuitive fashion and avoids much of the mathematics (Edgeworth expansions etc.) which are needed to provide formal proof that the bootstrap does what it is intended to do. Provides most of the important references up through 1993. For a similar treatment that is more current, see Davison and Hinkley (1997). Bootstrap Methods and their Application. Those interested in the theory and formal mathematics should consult Hall (1992). The Bootstrap and Edgeworth Expansion.

that's a very simple and clear book about bootstrapping They consider bootstrapping as a way to estimate confidence intervals and other properties of a statistical distribution. Its mainadvantages are a) it requires no knolwedge of the actual distribution(e.g., it does not have to be normal) b) in principle, it can be used with any statistics method and any solution.

This book is a comprehensive overview of modern dynamical systems that covers the major areas. The authors begin with an overview of the main areas of dynamics: ergodic theory, where the emphasis is on measure and information theory; topological dynamics, where the phase space is a topological space and the "flows" are continuous transformations on these spaces; differentiable dynamics where the phase space is a smooth manifold and the flows are one-parameter groups of diffeomorphisms; and Hamiltonian dynamics, which is the most physical and generalizes classical mechanics. Noticeably missing in the list of references for individuals contributing to these areas are Churchill, Pecelli, and Rod, who have done interesting work in the area of both topological and Hamiltonian mechanics. No doubt size and time constraints forced the authors to make major omissions in an already sizable book.

Some elementary examples of dynamical systems are given in the first chapter, including definitions of the more important concepts such as topological transitivity and gradient flows. The authors are careful to distinguish between topologically mixing and topological transitivity. This (subtle) difference is sometimes not clear in other books. Symbolic dynamics, so important in the study of dynamical systems, is also treated in detail.

The classification of dynamical systems is begun in Chapter 2, with equivalence under conjugacy and semi-conjugacy defined and characterized. The very important Smale horseshoe map and the construction of Markov partitions are discussed. The authors are careful to distinguish the orbit structure of flows from the case in discrete-time systems.

Chapter 3 moves on to the characterization of the asymptotic behavior of smooth dynamical systems. This is done with a detailed introduction to the zeta-function and topological entropy. In symbolic dynamics, the topological entropy is known to be uncomputable for some dynamical systems (such as cellular automata), but this is not discussed here. The discussion of the algebraic entropy of the fundamental group is particularly illuminating.

Measure and ergodic theory are introduced in the following chapter. Detailed proofs are given of most of the results, and it is good to see that the authors have chosen to include a discussion of Hamiltonian systems, so important to physical applications.

The existence of invariant measures for smooth dynamical systems follows in the next chapter with a good introduction to Lagrangian mechanics.

Part 2 of the book is a rigorous overview of hyperbolicity with a very insightful discussion of stable and unstable manifolds. Homoclinicity and the horseshoe map are also discussed, and even though these constructions are not useful in practical applications, an in-depth understanding of them is important for gaining insight as to the behavior of chaotic dynamical systems. Also, a very good discussion of Morse theory is given in this part in the context of the variational theory of dynamics.

The third part of the book covers the important area of low dimensional dynamics. The authors motivate the subject well, explaining the need for using low dimensional dynamics to gain an intuition in higher dimensions. The examples given are helpful to those who might be interested in the quantization of dynamical systems, as the number-theoretic constructions employed by the author are similar to those used in "quantum chaos" studies. Knot theorists will appreciate the discussion on kneading theory.

The authors return to the subject of hyperbolic dynamical systems in the last part of the book. The discussion is very rigorous and very well-written, especially the sections on shadowing and equilibrium states. The shadowing results have been misused in the literature, with many false statements about their applicability. The shadowing theorem is proved along with the structural stability theorem.

The authors give a supplement to the book on Pesin theory. The details of Pesin theory are usually time-consuming to get through, but the authors do a good job of explaining the main ideas. The multiplicative ergodic theorem is proved, and this is nice since the proof in the literature is difficult.

This remarkable book is by far the best rigorous introduction to many facets of the modern theory of (chaotic) dynamical systems. It introduces and rigorously develops the central concepts and methods in dynamical systems in a hands-on and highly insightful fashion. The authors are world experts in smooth dynamical systems and have played a major role in the development of the modern theory and this shows througout the book.

The book starts with a comprehensive discussion of a series of elementary but fundamental examples. These examples are used to formulate the general program of the study of asymptotic properties as well as to introduce the principal notions (differentiable and topological equivalence, moduli, asymptotic orbit growth, entropies, ergodicity, etc.) and, in a simplified way, a number of important methods (fixed point methods, coding, KAM-type Newton method, local normal forms, etc.). This chapter alone is worth the price of the book.

The main theme of the second part is the interplay between local analysis near individual (e.g., periodic) orbits and the global complexity of the orbit structure. This is achieved by exploring hyperbolicity, transversality, global topological invariants, and variational methods. The methods include study of stable and unstable manifolds, bifurcations, index and degree, and construction of orbits as minima and minimaxes of action functionals.

In the third and fourth part the general program is carried out for low-dimensional and hyperbolic dynamical systems which are particularly amenable to such analysis. In addition these systems have interesting particular properties. For hyperbolic systems there are structural stability, theory of equilibrium (Gibbs) measures, and asymptotic distribution of periodic orbits, in low-dimensional dynamical systems classical Poincare-Denjoy theory, and Poincare-Bendixson theories are presented as well as more recent developments, including the theory of twist maps, interval exchange transformations and noninvertible interval maps.

This book should be on the desk (not bookshelf!) of any serious student of dynamical systems or any mathematically sophisticated scientist or engineer interested in using tools and paradigms of dynamical systems to model or study nonlinear systems.

This remarkable book is by far the best rigorous introduction to many facets of the modern theory of (chaotic) dynamical systems. It introduces and rigorously develops the central concepts and methods in dynamical systems in a hands-on and highly insightful fashion. The authors are world experts in smooth dynamical systems and have played a major role in the development of the modern theory and this shows througout the book.

The book starts with a comprehensive discussion of a series of elementary but fundamental examples. These examples are used to formulate the general program of the study of asymptotic properties as well as to introduce the principal notions (differentiable and topological equivalence, moduli, asymptotic orbit growth, entropies, ergodicity, etc.) and, in a simplified way, a number of important methods (fixed point methods, coding, KAM-type Newton method, local normal forms, etc.). This chapter alone is worth the price of the book.

The main theme of the second part is the interplay between local analysis near individual (e.g., periodic) orbits and the global complexity of the orbit structure. This is achieved by exploring hyperbolicity, transversality, global topological invariants, and variational methods. The methods include study of stable and unstable manifolds, bifurcations, index and degree, and construction of orbits as minima and minimaxes of action functionals.

In the third and fourth part the general program is carried out for low-dimensional and hyperbolic dynamical systems which are particularly amenable to such analysis. In addition these systems have interesting particular properties. For hyperbolic systems there are structural stability, theory of equilibrium (Gibbs) measures, and asymptotic distribution of periodic orbits, in low-dimensional dynamical systems classical Poincare-Denjoy theory, and Poincare-Bendixson theories are presented as well as more recent developments, including the theory of twist maps, interval exchange transformations and noninvertible interval maps.

This book should be on the desk (not bookshelf!) of any serious student of dynamical systems or any mathematically sophisticated scientist or engineer interested in using tools and paradigms of dynamical systems to model or study nonlinear systems.

It is always nice to have an example. This is what this book gives you. It also gives you options. SAS commands are written clearly. It still can not be your only logistic regression book. You will need other books to have a better understanding of logistic regression.

If you need to understand logistic regression analysis and you must do it in SAS, then you must have this book.

Gives clear, concise explanation of logistic regression, how to accomplish it in SAS, and explains the details of the SAS results.

This book had me up and running in short order.

I had used this author's SAS book on survival analysis before, so I was expecting another virtuoso performance when this book came out. And I was not disappointed. The book is incredibly useful, provides clear examples, makes you feel like you really understand the statistics, provides voluminous SAS code to illustrate how to implement analyses, and even teaches you a few tricks about how to handle unusual data problems along the way. Highly recommended, even if you don't use SAS!

This book is a gem, not so much for the results it contains, but for how it presents them. The authors introduce a compact notation centered on kronecker products and schur products. In addition they present a useful set of operators and identities for manipulating these objects. The result is a pleasing language for mathematical manipulation avoiding to a large extent the "debauchery of indices" that pose a problem in many investigations. This presentation makes the price of the book almost worthwhile. The review of the properties of matrices is compact and reasonably complete. The applications are straightforward and competently presented.

the authors provided a really nice treatment on the not-so-popular subject. The presentation is clear and readable. I just want to second the review below : the typesetting quality is inferior to a photocopied book ( or one may feel it's like a book published in 70's)

For some reason, in spite of its enormous utility, matrix differential calculus is oddly absent from standard courses in signal processing and control. The great strength of this text is its focus on the development of sufficient and necessary conditions for constrained/unconstrained minima/maxima. There are good examples regarding maximum likelihood estimation. There are also some useful results regarding the Kronecker product and commutator matrices. The chapter at the end covers specific topics in econometrics. The paperback edition suffers from completely hideous typesetting that is exacerbated by some of the notation. In spite of the fact that the paperback is not cheap, the pages look like they were photocopied. Maybe I just got a bad copy. I don't know if the hardback edition has the same problem.

This little book can take a bright high school student or undergraduate through some most interesting territory, previously uncharted at this level of mathematical understanding. Fixed point methods -- approaches to problem solving based on the simple equation f(x)=x -- are ubiquitous and ideally suited for digital computers, but they are seldom introduced in the classroom. Get this book for your high school library. Use it as an independent study for that student you don't want to set loose on calculus quite yet.

This short book is a gem. Metric Spaces is an especiallywell-written introduction to analysis. I liked the author's informalconversational approach to a rather abstract topic. Although I did find it necessary to reread substantial sections for full understanding, I found Metric Spaces to be quite enjoyable.

Dr. Bryant motivates the reader immediately with a look at iterative techniques, fixed point functions, converging sequences, and approximation solutions - all in an engaging style. Later topics included distance concepts, function spaces, and the relationship between closed sets, complete sets, and compact sets. The fourth chapter was devoted to the contraction mapping principle and its use in solving differential equations.

Is this book for you? The author says: "The only prerequisite is to have done a course on elementary analysis: it is not a prerequisite to have understood it nor to have remembered it at all." I personally had no formal courses in real or functional analysis and the highly structured axiomatic approach of analysis texts had never appealed to me. I only had a vague idea as to the properties of a metric space. But I was lured into buying Dr. Bryant's short text by the previous Amazon reviewers. And thankfully so.

Dr. Bryant clearly enjoys his subject, but he just as clearly recognizes that not everyone might have such an abiding interest. Throughout the text, he points out opportunities where the reader might skip forward if the going has become less interesting. (For the record I refused to be enticed by these short cuts.)

Problems are embedded in the text, one or two at a time, and are used to reinforce points under discussion. Most have clear hints and I found many problems straightforward, but others were more difficult. A few problems were identified as appropriate for the "keen" student.

The most abstract mathematics are reserved for the last (optional) chapter, but the author does encourage the reader to stay with it: "It would be a pity to stop ..." Chapter five recasts the first four chapters into a more generalized form of real analysis and addresses the question: "What makes analysis work?"

Dr. Bryant had an unusual goal for a mathematics text. "I have tried to provide a readable and natural introduction to an abstract subject in a down-to-earth manner." Also, he says, "My aim is to provide a book which can be read and enjoyed ..." He succeeded in doing just that.

A short but excellent book for someone who wants a well motivated refresher on analysis. By grounding the ideas with applications of fixed point theorems (such as proving the existence of unique solutions to certain types of differential equations) the author makes accessible an area of mathematics that is often treated in an axiomatic and uninteresting way.

I believe the author is correct when he recommends the book for people who have already had some exposure to analysis. At best a student should already have completed the standard non-rigorous college calculus sequence to get the most out of this book.

This text is one of the few to make the work of Andersen et al. (Statistical Models Based on Counting Processes, Springer, 1993) accessible to the average statistician. It has three limitations:
1) fails to mention the use of permutation tests for hypotheses regarding the Nelson-Aalen estimator,
2) fails to cite Good PI, Globally almost most powerful tests for censored data,Nonpar Statist 1992, 1:253-262.
3) fails to deal with multiple dependent events (the most common case).
The text also fails to be prescriptive; one is often left feeling that all tests are equal which simply isn't the case.

As a biostatistics PhD student I've had to endure many very poorly written textbooks (though there are many good one's too). Not only is this book a great text on applied survival analysis, it's a great piece of statistical writing and should be used as an example for all applied texts. The general approach of introducing the theory followed by examples with SAS/SPlus code makes learning the material easy and fun. I wish all statistics texts were even half this good!

Terry Therneau is a research statistician at the Mayo Clinic and Patricia Grambsch is a Professor of Biostatistics at the University of Minnesota. The Cox proportional hazards model has been one of the key methods for analyzing survival data with covariates for the last 25 years. Proportionality is a key assumption that limits its use. There has long been a need to find methods which diagnose when the hazard rates are not proportional and provide alternative methods in such situations. Using the theory of counting processes the authors are able to extend the Cox model to more general situations including multiple/correlated event data using either marginal models or random effects (frailty) models. Time dependent covariates are also covered. Some of the theory of martigales and counting processes is included to make the book self-contained. Generalized residuals are used to identify outlying and influential observations (analogous to ordinary regression) and also to assess the proportional hazards assumption.

Although the topics are advanced and the mathematical level is high the book is designed for practitioners, emphasizing applications and providing numerous examples, many from the authors' experience. Statistical analyses are done in SAS and SPlus. The authors tend to use SAS for data management and analysis and SPlus for diagnostics and other plots. Therneau is an expert programmer who has written much of the necessary software in both systems.

Therneau gave an excellent short course that I attended a couple of years ago at the Joint Statistical Meetings based on a draft of the text. The finished product is as good as I expected.

The appendices include SAS and S-Plus tutorials on survival analysis and provide SAS Macros and S functions to apply the new methodology.

This is a fantastic book on using S-Plus. I would have given it 5 stars had its treatment been given in a more clear way. Nonetheless the S language is well presented and many statistical analyses are sampled using S-Plus. In fact, this is also a strong applied stats book, with S code given for each topic.

This is an *essential* tool for anyone using S-Plus. The book is well laid out, supplementing and complementing the manuals. In addition, the authors provide libraries that expand many of the routines in S-Plus. If you use S-Plus and do not program directly in S yourself, this book should be on your shelf.

this is the 'bible' of numerous statistician using Splus or R. Ripley and Venables are two of the builders of S/Splus and they're still involved in many projects regarding this topic. The second edition is very complete and make Splus computing easier.

Weaver's book (actually written in the early 1960's) is becoming somewhat dated. Since this was before calculators were readily available, the book's examples don't take advantage of the readers access to powerful number crunching such as factorials and exponentials. Having taken over 4 years of calculus in college, I don't consider myself mathematically challenged, but I often had a hard time following the author's reasoning and his sample problems. Those without a good mathematical background could easily get lost and discouraged in this book unless they just skimmed over the rough areas and picked up those subjects and anecdotes of possible interest to them. For a quick read of probability theory at a low price, this isn't too bad; but more modern books are available.

The main strength of Mr. Weaver's "Lady Luck" lies in its sheer readability. Mr. Weaver is very careful about presenting his arguments so that they may have maximum intuitive appeal, while at the same time refusing to compromise the mathematical rigor that is necessary to construct any serious theory of rudimentary probability. What is most important about the work is that it provides the reader an extremely entertaining and well written framework for thinking about questions of probability. A concept such as "independent random variable" which a mediocre statistics textbook may quickly skip is a surprisingly philosophically complicated idea, and has troubled academicians, let alone lay people. Mr. Weaver's work, far from being in any sense "slow," deals with how we are to take into account this very basic ideas that form the starting point to this particular area of the mathematical sciences. Finally, Mr. Weaver's references to distinctively late 50s early 60s phenomenon provide an entertaining look at the thoughts of the time.

I could not put this book down. The author, Warren Weaver, writes in a very unpretentious, personal voice. He unravels the complex subject of probability in a manner that is both encouraging and challenging. The reader develops a personal intuition for applying basic probability formulae (with careful consideration of relevant factors and an increased sense of self-confidence). I believe this book could be understood by any person familiar with basic algebra. On the other hand, the average physics PhD would likely find it equally interesting, because its intuitive approach is so refreshing.

This is a quick reference book (VERY small and thin)for matlab commands. Has come in handy in getting a quick fix to some problems/equations. BUT you have to know what you are looking for already.

Excellent text. Contains nice, brief summaries of matlab functionality, with references on where to go to get more verbose explanations. This book is about the size of a postcard. When I need to locate or discover a new function that I'm sure matlab has somewhere, this Primer is usually more effective than the matlab on-line help. Also good for an overview of functionality, an area where Matlab's on-line help is particularly weak. Assumes no knowledge of matlab.

In MATLAB, really the only thing you need is a list of commands, then you can type 'help command' and be done. Sigmon's book is just this. Good enough to carry me through undergrad to first year grad school. Next year I'll need a reference specializing in the controls toolkit, but this Primer will still be glued to my hand whenever I start Matlab. I'm buying one for each of my students.