Paul Wallace offers an engaging and insightful examination of the prospects of an aging society. The title that wallace has chosen hints at the magnitude of the anticipated changes of a 'greying society'. Wallace makes extensive use of UN and OECD studies to wake up anyone who doubts that an aging society is not an issue that ought to concern them, their finances and business. For the more serious minded, there are plenty of specialist texts to consult. For a broad overview of the likely impact of an ageing society, wallace's contribution is timely and accessible since he spares us of a lot of technical jargon that may intrude (economists usually have plenty to say on this topic!).

Wallace constructs his analysis by building on the theme of his choosen title. Part one, explores the 'faultlines' that major studies identify, and awaits explanation in the next section, the 'tremors'. If you understand the logic of the opening chapters, then the 'shockwaves' should not be a big surprise. I suspect we'll have to live through it before anyone truly understands it. This is partly a get-out clause for political leaders who ought to urgently set out plans for, for example pensions reform, but since people and politicans don't tend to think and act for the long term, you can imagine wallace urging disapproval.

Each facet of life merits a mention; from ageism in the workplace, to the effect on property prices, spiralling healthcare costs and the impending 'pensions crunch'. While many of the current baby boomer generation are looking forward to early retirement, wallace leaves us to wonder if we, the younger generation, will have to work so much harder to share their (modest) ambition of enjoying their leisure after 40 years of labour?

A good read.

When I moved into France from my native Colombia, I was astounded at the number of old ladies with dogs and the paucity of mothers with babies and teenagers. Agequake confirms that this perception is just another aspect of the huge changes that new demographics will bring on within the next 20 years. The good news is that people are living longer. The bad news is that they are retiring at a younger age. And that, the longer they live, the more medical care they will require.

Wallace shows that the dependency ratios (the ratio of non-working "dependents" to working folk) will lead to a point where each worker will need to support not only himself, but a pensioner as well, and his own children, if any (and there will be very few). As the number of young working people, usually the more creative of all age-groups, continue to shrink, innovation will also came to a halt, and ultimately economic growth will vanish and then reverse the secular growing trend. While some environmentalists may feel overjoyed by this implosion of capitalism, most of us who rather liked material comforts and hoped that they would continue to grow endlessly will be less satisfied. The impact of the "agequake" will be felt in every sphere. Corporate hierarchies will make less sense when there are more middle-aged managers than young newcomers. The relationship between youthful and aggressive Third World Countries and rich older OECD countries (where elderly women will be the most influential constituency) will be fraught with dangers. Share prices will tend to collapse as the "Baby Boomers" start to retire and prefer to liquidate some of their assets. The housing market will be altered beyond recognition.

What can be done to avoid this future? Unsurprisingly, not much. Government policies cannot permanently improve fertility in rich countries, immigration in the scale required to make up for the shortfall of young workers will be politically indefensible, and the growing importance of older voters makes it virtually impossible for politicians to effect changes in fields such as retirement ages, pensioners' rights
or public health.

All in all, a sobering read. When these things happen, those of us who read it will have at least a headstart on everyone else. Not bad for a few bucks, eh?

A modern view of the rollercoaster of the stock market, this book really describes just what the market can be like. One day you're riding high, and the next you can be in the pits!

This tidy book is a highly readable, introductory survey to the topic of modern time series analysis. It excels in its ability to focus on the more intuitive aspects of analysis and model identification. The discussion of both time- and frequency-domain approaches is reasonably balanced, and Kalman filtering is also introduced. While it touches on many modern aspects of time series analysis, it sometimes (intentionally) lacks important technical depth necessary for implementation.

The author has done an admirable job at keeping the book manageably small. However, the reader is occasionally left wanting where interesting details are omitted because the author considered them "beyond the scope" of the book. For example, the preface mentions that several new topics are incorporated into the 5th edition (wavelets, for example), but the reader only finds a gratuitous single paragraph with references to complementary journal articles. In these few rare cases, the discussions are not intuitive enough for the reader to know whether it would be profitable to bother with further research at the professional journal level. Still, this title does well to reference the most important landmark works in the time series literature. Those performing remedial research may find it is easier - and more productive - to simply consult Chatfield's recommendations of important topical works before resorting to online or library literature searches.

This text has been in print since 1975 with new editions arriving every 5 years or so (perhaps even a 6th edition is close, since the last edition is copyrighted 1996). I am usually suspicious of textbooks having increasingly larger numbers of editions because the continual re-writing implies some level of recurring insufficiency. However, the frequency of update is probably justified due to continuing advances in this field of study. As a result, this title is surprisingly current given its introductory status (although the 4th and 5th editions do not differ too much).

For someone new to time series analysis, this may be one of the better places to start, especially for the price. Readers lacking in intuition or experience in time series analysis - especially non-statisticians - will certainly appreciate this introductory title. The more experienced analyst will also be well served by the author's expert perspectives - but to do practical work, this text will still likely need to be supplemented. The generous citation of additional literature will help the reader to know where to go next.

When I was a graduate student at Stanford my advisor taught an elementary time series course out of Chatfield's book. It was either the first or the second edition. I was his teaching assistant. The book has been very successful and is now in its fifth edition. It covers most of the important topics concisely and in an intuitive manner. This book gives the student a feel for time series analysis and an appreciation for its applicability. It is not meant for someone who wants a rigorous treatment and a strong understanding of the theory. For that the text of Brockwell and Davis or Anderson or Brillinger or Priestley are more appropriate.

Realmente explica de manera clara, conceptos básicos en series de tiempo, por lo cual lo recomiendo para cualquier persona, así conocera el fantástico Mundo del análisis estocástico en el tiempo...

Cohen and Cohen's MRC analysis book is well versed and easy to understand for someone that is familiar with MRC terminology, however, for first year graduate students, the text is very equivocal. The book is lacking ample illustrations of complex problems, leaving students to rely on outside sources. Also, the book uses unfamiliar symbols that do not correspond with other MRC books, which intensifies the confusion level of the students even more.

Overall, the text is a great addition to a statistical library, and this reviewer recommends it, in spite of being a sub-par book for first year graduate students.

...This book is the source of all you need. It's hard going at times, but so's the subject. The book's 15 years old and remains the best guide to the analysis of correlated data. It's a reference book, one I value as much as a good dictionary. To use it as a text would be misguided unless the instruction was aimed at a sophisticated audience.

I agree with the previous reviewer that there are times when the exposition in the book gets a bit intense; but c'mon! We're dealing with statistics. You gotta sweat a bit. That's when learning happens. In my opinion the book is extremely clearly written. And although you may have to re-read a few sentences a few times, the basic tools for understanding most every major aspect of MRC is embedded in the text. In sum, this was a great book that I read as a 2nd-year graduate student in psychology. Unlike the first reviewer, I turned to this text when I got confused during the course lectures!

I recommend this book to anyone who wants to develop a deep understanding of Browninan Motion and Stochastic Calculus. However, the level of detail and rigor can obscure the main ideas, and so it is a very difficult introductory text for readers without a strong background in probability theory and continuous Markov processes. As a teaching assistant in a Mathematical Finance Masters program, I recommend that my students read Oskendal's Stochastic Differential Equations first, which gives an excellent introduction to the material without sacrificing rigor.

The theory of Brownian motion is ubiquitous in physics and mathematics, and has recently become very important in mathematical finance and network modeling. The observation of the irregular movement of pollen suspended in water by Robert Brown in 1828 led Albert Einstein to formulate a theory for Brownian motion. In this book the authors outline rigorously the theory of Browian motion. Their logic is impeccable, and the content is fascinating reading, even to those very experienced in the subject.

The authors begin in chapter 1 with the task of defining martingales and filtrations, with the notion of a stochastic process being adapted to a filtration taking on particular importance. They omit the proof that a process is progressively measurable if and only if it is measurable and adapted, because of the difficulty of the proof, but give a reference where the proof can be found. Continuous-time martingales are defined, with (compensated) Poisson processes given as an example. The Doob-Meyer decomposition and square-integrable martingales are discussed, and the chapter if full of exercises, with solutions provided to some of these at the end of the chapter. Brownian motion is formally defined in the next chapter, with its existence proven using Wiener measure on the space of continuous functions on the positive half line. The discussion in this chapter has to rank as one of the best in print, due to the meticulous and precise manner in which the material is presented. The Markov property of Brownian motion is proven, along with a good presentation of the Levi modulus of continuity. Readers working in constructive quantum field theory will see their usual construction of Wiener measure in the second exercise of the chapter. Those working in that area are used to seeing (conditional) Wiener measure defined on a collection of cylinder sets, which is then extended to the Borel subsets . Such a construction is done in this book, but the approach is somewhat different than what physicists normally see in quantum field theory.

The theory of stochastic integration is presented in Chapter 3, and it is superbly written. The authors are careful to distinguish the theory of integration for stochastic processes from the ordinary one with emphasis on the actual computation of stochastic integrals. The reader is first asked to explore the Stratonovitch and Ito integrals in an exercise., and then a thorough treatment is given by the authors later in the chapter. The authors point out the differences between the Ito and Stratonovich integrals, with the latter being defined for a smaller class of functions than the former. The important Ito rule for changing variables is discussed, and then used to give the Kunita-Watanabe martingale characterization of Brownian motion. Physicists involved in constructive quantum field theory will appreciate the discussion of the Trotter existence theorem in this chapter.

The connection of Brownian motion with partial differential equations, so familiar to physicists via the heat equation, is the subject of the next chapter. These equations give the transition probabilities of the stochastic process, and are studied here first in the context of harmonic analysis, namely the classical Dirichlet problem. This is followed by a beautiful treatment of the one-dimensional heat equation and the Feynman-Kac formulas. Those readers working in constructive quantum field theory will see the Green's function lurking in the background.

The very important topic of stochastic differential equations is outlined in chapter 5, with emphasis placed on the study of diffusive processes. The solutions of these equations have an immense literature, and the authors do not of course overview all of it, but do give a useful introduction. Both strong and weak solutions are discussed, with the Girsanov and Yamada-Watanabe techniques used throughout. Explicit solutions are given for linear stochastic differential equations, such as the Ornstein-Uhlenbeck process governing the Brownian motion of a particle with friction. Financial engineers will appreciate the discussion of the applications of this formalism to option pricing and the Merton consumption theory in this chapter. Options pricing is cast in martingale terms, and then the usual Black-Scholes equation is derived from this. The notorious Hamilton-Jacobi-Bellman equation is discussed in the consumption/investment problem, and the authors show how to employ techniques for solving this problem instead of solving this difficult nonlinear equation. The authors give a hint of the important Malliavin calculus in the Appendix and give references for the reader.

The last chapter of the book is more specialized than the rest and deals with the Levy theory of Brownian local time. This theory does have a connection with the theory of jump processes, which are currently very important in financial and network modeling. The authors do a fine job of explaining how Poisson random measures permit the event bookkeeping in these jump processes. Their discussion is applied to the computing of the transition probabilities for a Brownian motion with two-valued drift.

Before young talents try to prove themselves in this area they must know what is known... Karatzas and Shreve offered an intensive, rigorous and covering text on stochastic processes. Especially needed for quants working in the hottest applications, economics and finance, where demand is rapidly growing, but the qualifications should essentially be based on "Brownian Motion..." Warning: the book is for advanced readers; be ready for some overwhelming notations. Harder-than-usual efforts are required, by they will ultimately pay off.

This is an extremely valuable compendium of what almost any pracitioner needs to know about 40 of the most commonly used statistical distributions. It is designed as a quick lookup reference for each of the distributions. Most chapters begin with a few brief lines describing some of the applications of the distribution, and then provide a list of relevant formulae, such as for the distribution function, probability density, moments etc. Relationships to other distributions are defined, means of estimating the parameters provided, and ways of generating random numbers from the distribution are indicated.
Graphs of the distributions are shown with varying parameter values in most cases.

The book should be seen purely as a handbook on statistical distributions, not as a theoretical reference. The book is ideal for those who make use of statistical distributions in other fields, and who are not necessarily statisticians themselves. I have no formal statistics training, but use distributions extensively in my own work, and found this book very easy to understand. I have been using Johnson and Kotz monographs fairly extensively as references for the distributions in which I am interested, but find this book a much simpler reference for the basic facts of the distributions. In addition, its consistent use of notation across the chapters makes it much easier for the reader to cross reference.

I refrain from giving 5 stars to the book because of a few weaknesses, primarily omissions. Firstly, as an earlier reviewer pointed out, the lack of an index is a little annoying sometimes. Secondly, the bibliography is very slim, and so the reader interested in finding further details, proofs etc., is given very little direction. Thirdly, there are a few obvious omissions, such as the cumulative distribution function for the chi-squared distribution. Fourthly, random number generation is described only when the generation is relatively simple (for example, a method for generating random variates from a gamma distribution is described only for special cases). Finally, I would like to have seen more guidance provided in the sections on parameter estimation, such as first and second derivatives of log-likelihood functions when the estimates have to be derived iteratively.

This is the most thorough reference on distributions that I have found. The information contained about each distribution is concisely stated in a few pages - you would probably have to look in several books to get the same material. Most useful to people writing digital simulations is instructions on how to generate the distribution using random number generators. This is especially useful if you don't have access to statistical software packages. Lack of an index detracts, but is minor. Listings are alphabetical, by distribution name, so you might have to page through the book to find one that is not in an obvious location (like continuous uniform is listed as "rectangular", but discrete uniform is listed as "discrete uniform"). You need to be familiar with basic statistics to understand the book; but you don't have to be a statistician.

This is a very good reference for univariate statistical distributions. It provides maximum likelihood and moment estimation formulas for many distributions i'ven't seen anywhere else. That's right that the price is quite high for the number of pages but this information is invaluable for practitioners who need to fit distributions to real data.

The book covers most of the Statistics. The problems are very well explained and the explanation covers the theory too. The book does not cover sufficiently neither Regressions nor Anovas. Times Series and BioStatistics are covered at a high school level. BootStrap - Monte Carlo, is not even mentioned and Stratified Samples are almost not touched

Nevertheless this is a valuable book that will deepen the statistical knowledge of the reader. I would recommend it to any one that has done some intermediate statistics and wants to consolidate his knowledge. Graduate students will not be helped much from it.

I wish REA publishes a second volume with the chapters that are not covered here.

This book was very helpful to me during the beggining of my graduate course of Statistics I. It does help with most of the basic stuff but it does not contains many good examples of ANOVAS, Latin Squares, Randomized Complete Block Design, Blocking and other advanced topics (It doesn't contains any exercises using Tukey, LSD, Dunnet etc). Anyway, it is still a good book and it's still a good buy.

One of the best investments anyone in a statistics course can ever make. I was lost in a graduate level statistics course and this book not only pulled me through but I finished with flying colors. These books start with the simplest problems and explain their solution each step of the way. You stop doing problems for a particular subject when the problem solver gets to a level they're not covering in class. I found the class text useless. You will not end up doing more work. Well worth the expense.

consistence,stability and convergence of multistep numerical methods for ordinary differential equations.

This is a mathematics book, not a cookbook. It's worth buying this classic just to read the hidden sentence formed by the first letter of each sentence in their preface.

This is the classic work that explains in detail why numerical methods perform well or poorly. It's the best book I've ever read on Numerical Analysis. Great problems too! From Numerical Linear Algebra to PDE's the basic theory is explained beautifully. If you've ever wondered why iterating the corrector in the predictor-corrector method for solving ODE's doesn't do any good this is the book for you. As an inexpensive Dover paperback this is a real bargain!

A good, cheap overview of logistic regression analysis.

I bought and I'm glad I did, but I don't refer to it like I do Hosmer and Lemeshow's text.

Prof Scott Menard must be commended for writing an excellent book on Logistic Regression. Explaining it in the context of commercially available software packages is a very good idea. I was able replicate some his analysis using SAS on the data set used in this book (available on line from ICPSR, Univ of Michigan).

I eagerly await the next edition of this monograph. Thank you!

As its title suggests, this book is an excellent guide to using logistic regression in data analysis. I purchased this book because I needed to do several logistic regression runs for my dissertation. It turned out to be an extremely useful book for two reasons. First, it presents logistic regression alongside more traditional ordinary least squares (OLS) models. Therefore, if you already have a good understanding of OLS models, this book is very easy to follow. Second, its discussion of logistic regression issues in the context of SPSS or SAS makes it very easy to follow along with your own data analysis as you move through the book. Since statistical packages are always improving, this does date the book a little. However, this is a very minor concern. I believe Dr. Menard is to be commended for including issues regarding popular software packages in this work.

When compared to SAS's documentation, this book's greatest advantage is explaining in english (rather than mathematical notation) the assumptions and limitations of SAS's (and SPSS'S) algorithms. Its chapter on logistic regression diagnostics is alone worth the price of the book. In short, if you need to use logistic regression analysis and you already understand OLS, you cannot go wrong with this book.

This book skips some important basic concepts and has several poor, glossed-over explantions. I'd recommend Neter or Mendenhall.

I used this book for a second level statistics course for my Master's degree in Epidemiology. I liked it!

All the underlying math you want to know is sitting on the pages, clearly explained though examples with computer output and graphs. I worked through the problems in the text without difficulty and reproduced their work. I understood what I was doing. Each chapter is followed by a series of problems. You probably want to get a solutions manual if you want to check your answers.

The material covered includes: Univariable and multivariable linear regression, correlations including multiple partial, ANOVA, ANCOVA, Polynomial Regression including orthogonal polynomials, dummy variables, selecting best regression equation, and introductions to repeated measures ANOVA, maximum likelihood methods, and logistic regression.

Now that I feel that I have these basics under control, I would like a book on "approaches" to data and dealing with "difficult" data. This book contains one chapter on regression diagnostics -- not enough. But I guess that is the next step....

Other readers have commented on other books addressing the same topic, unfortunately I have not read those other books. However, I am certain that you will learn from this book, and when you are done, you will be ready for more.

(Did I mention that I signed up for a course with Dr. Kleinbaum on analysis of matched data?)

The most complete and cristal clear exposition of multivariate analisys I ever read.

After about 30 years this book is still considered a classic worthy reading in details. Neatly called "Box & Tiao" in this reprint edition, it shows how this book came to be widely known in the tight Bayesian circle, which now grows at an exponential rate. I was looking for a Bayesian book which can tell you how you can do the Bayesian approach, NOT why you should do it. (The latter was needed before, but has become irrelevant in practice.) This book started with a good promise of working out a number of standard statistical problems using the Bayesian approach, it concluded with a marvelous job of accomplishing that huge task with details and accuracy. I generally agree with Stateman13's review in that this book really elaborates Jeffreys noninformative Bayesian approach, which I think should be the preferred approach. However, I consider this book one of the best Bayesian books avilable, even better than some of the much newer books. (Tendency to rush publication will not get it done.) This book is a labor of pains but is an artwork worthy your precious bucks!

This is a book written in 1973 but showing the brilliance and forethought of George Box. Wiley reprinted it in its popular paperback classic series in 1992. The first few chapters introduce Bayesian ideas and show how with noninformative priors the Bayesian results resemble the classical frequentist results. This essentially reviews the work pioneered by Harold Jeffreys.

In the latter chapters more complex problems are introduced including many that do not have nice classical solutions. Box and Tiao show how Bayesian methods contribute ideas that provide new insights into these problems. The discussion of hierarchical models anticipated the developments in Bayesian methods that occurred in the 1990 when the MCMC methods burst onto the scene.

This book is nice for a historical perspective but anyone seriously interested in doing modern Bayesian analysis needs a book that deals with the MCMC methods and there are many nice books available these days.

Have you ever wondered about the origins and meaning of statistical concepts? Most of the books on Statistics shy away from this topic, they just throw formulae at you! Not this book. In the first two chapters it goes to great extent to explain a very important concept of noninformative prior. It also states very clearly the differencies between more traditional Sampling Theory approach and Bayesian Analysis. While majority of Statisticians prefer the ideas and notions of Sampling Theory, majority of Scientists and Control System Engeneers are more inclined to use Bayesian Analysis because of its practicality. This book gives a plenty of material on Bayesian Inference and shows how to put theoretical knowledge into practice. It presents the material in transparent and orderly fashion but it requires certain degree of mathematical sophistication. A prerequisite would be any standard text book on Statistical Inference.