Monte Carlo methods are old. They can be traced back to Buffon's needle problem in the 17th century. However meaningful application had to wait for the invention of digital computers in the 20th century. Much of the development took place in the 1940s and 50s for military and nuclear engineering application. The Hastings - Metropolis algorithm of the 1950s has had a rebirth in the 1990s with the application of Markov Chain Monte Carlo methods to imaging problems and many Bayesian problems.

The authors of this book are Bayesians and present Bayesian methods in the very first chapter. The book is intended to be a course text on Monte Carlo methods. I judge the level to be intermediate to advanced (first or second year graduate level). The first chapter introduces statistical and numerical problems that Monte Carlo methods can solve. It includes a discussion of bootstrap methods in the notes at the end of the chapter. Chapters 2 and 3 introduce standard topics including methods for generating pseudo-random numbers and various variance reduction techniques. Chapter 4 is an introduction to Markov Chains. Markov Chains are commonly a topic in introductory courses on stochastic processes. The authors presuppose that the reader has no knowledge of Markov Chains. So they develop the essential aspects of the theory needed in the application of Markov Chain Monte Carlo methods (MCMC). Chapter 5 then deals with optimization problems discussing simulated annealing, stochastic approximation and the EM algorithm. Chapters 6 - 8 deal with topic in MCMC methods. The final chapter deals with applications to missing data models. The topics are very current and important to statisticians. The theory is covered very well. Many interesting examples are provided throughout the book. A number of these are presented in the problems section at the end of the chapters. It also contains a very extensive bibliography.

I have a graduate level physics and mathematics background really enjoyed the clear descriptions as a useful and ever-needed review.

Monte Carlo experts who want to apply their knowlege to finance should also read: "Options, Futures, and Other Derivatives (5th Edition) by John Hull; and "Credit Derivatives" (2nd Edition) by Janet Tavakoli.

This text may or may not be the best book on MC for a particular application; to be honest, it's the only book on MC I own.

However, I did peruse a number of texts before I bought this one, and I am very pleased with my decision. To me, this book does something that seems necessary but is relatively uncommon: it gives a detailed, modern, comprehensive introduction to MC methods per se. There are other texts that might have one of those characteristics, but they seem to either not have all of them: they either are not modern, not comprehensive, not introductory, or are not concerned with Monte Carlo per se.

Many other excellent texts, for example, are largely oriented toward Bayesian implementations, or general integration, but not both.

I would highly recommend this book as an excellent introduction to MC methods as a general computational tool.

The three authors have done a superb job of conveying their message. However, I feel that the book is a little over-drawn and long. It is still an excellent example of the supreme books that Springer releases. Congratulations to this BRILLIANT trio!

This book is a great collaboration. Hedayat is a statistician who has been a major developer of orthogonal arrays for statistical experimental designs, Stufken was a student of Hedayat who has worked with him on this research. Sloane comes from the mathematics of codign theory where he has developed orthogonal arrays to produce error correcting codes.

Orthogonal arrays were introduced by C. R. Rao in the 1940s. He developed them for applications to experiments. He also produced the seminal work and some important inequalities constraints for such arrays. Rao provides a great endorsement for this book in the forward he writes.

Many statistical texts on experimental designs include orthogonal arrays. Some books on coding theory include them also. However this is the first book entirely dedicated to orhtogonal arrays and their properties. Thus are married the major and diverse areas of applications. Orthogonal arrays are also of interest to mathematicians who work in abstract algebra because of the connection between the orthogonal arrays and Galois fields.

The authors provide an historical account of the field, the many applications including the revival in the 1980s through the robust designs of Taguchi whose fractional factorial experiments were all orthogonal designs.

The authors presentation is clear. They provide a clear definition of orthogonal arrays and give a thorough and up-to-date account of the literature. The text contains over 650 references. This is a must have book for anyone specializing in coding theory or the construction of efficient statistical designs.

Stufken and Hedayat have put together a book that is beyond words. It is one of the few textbooks dealing with Orthogonal Arrays that I've been able to fully understand. Congratulations Stufken and Hedayat

I had to buy this book for a class for my EE degree- I must say as much as I hate probability and the like, this book was one of the best on the subject that I've ever come across. It really feels as though this is a 'friendly' book, just like it says in its title. I didn't give it a full five stars, because I think it should include a few more worked out examples per section. Nevertheless, it still has a fair number of worked examples and also quizes which are solved at the end of the book. All and all, I really enjoyed the book and consequently the course.

Professor Yates presents this subject matter, which may be somewhat confusing at first blush, in a manner that is easy to understand. This approach is very different from other texts, particularly those geared toward mathematicians, which tend to be overly terse and abstract.

I taught an undergraduate course in probability and stochastic processes last summer using this book. This book is so clearly written and laid out that for the first time in 20 years of teaching I could lecture directly from the book rather than having to spend the time to make up a set of notes. The many worked out problems are very helpful in illustrating concepts. All in all a great book.

A nice review of MLE-based methods for categorical, limited, and ordinal dependent variables. Most social science data is best thought of as categorical, ordinal, etc., not interval, and so a readable treatment of one approach to the analysis of such data that does not rely on intervality assumptions is worthwhile.

The author has a very clear explanation of topics such as how MLE works, some numerical methods for maximizing, various tests associated with MLEs, etc., all written at a intermediate level. It's not too advanced so readers won't be driven off but also isn't a cookbook. Lots of nice examples throughout.

It's definitely in standard regression mode, which is not to say bad, just limited. It doesn't cover (or indeed discuss) topics such as categorical multivariate analysis, alternate loss functions for estimating categorical or ordinal regressions, including alternating least squares approaches or quantile regression, categorical or ordinal time series, or instrumental variables.... It's not the last word on the topic, but is certainly a solid first word.

Since I do statistical modeling in industry, I was looking for a good book on Logistic regression that would give me a deep understanding of the subject; one that also had wide coverage (Poison regression, Tobit models, ..etc.). I decided on J. Scott Long's book, after considering Applied Logistic Regression by Hosmer and Lemeshow, and Limited Dependent and Qualitative Variables in Econometrics by Maddala. I must say I am very pleased with my choice. The topics are very clear, and the math is used as an aid to understanding, and you don't get bogged down in the math. It is a pleasure to read the book.

This book is especially useful to start understanding topics like ordered probit, multinomial logit, negative binomial regression and zero-inflated count models. Although it starts with a chapter on the linear regression model, it should not be mistaken for an introductory text. I would certainly advise readers with limited background in regression models to start with other books, like the one of Wooldridge (Introductory Econometrics). The quality of this book must be that I've yet to see a book that explains these topics more intuitively. That is not to say it is easy or without mathematics, it's not. It just looks like the mathematics is only used for better comprehension, not to give you the full proof. Furthermore, while reading it you get the feeling that the author understands what you, as a researcher, are interested in. This allows him to focus on the topics of interest, like model selection and testing and interpretation of output. So although this is not a cookbook, it may well be the closest thing to it, especially in combination with his new book on applying these models in Stata. It is a pity that the author stops short of non-parametric models (next edition?).

I had to use a book written by the professor teaching me probability. His book being very poor quality I decided to try this one instead. With success, it is a very clear and compact book. The only thing I found is that it does not cover enough distributions, and that there are quite a few typos.

I purchased around $200 worth of textbooks on Probability and Random Processes for a graduate level course I took. Of those books, this one got the most use. It summed up topics concisely and provided adequate examples with enough detail to actually show me how to get an answer. If you are getting regular textbooks on this topic, it would be well worth your while to pay the extra small price for this handy guide. It will be well worth the money.

After being out of school for ten years, this book proved to be essential for my M.S.E.E. course in Random Signals. This book concisely summed my undergraduate engineering Signals class with solved problems. In addition, the solved problems acted as a guideline for my Master's class. I felt that I could not have gotten through my class without this book.

The reason why I ordered this book in the first place was the advice of my professor. My professor is a Ph.D. in the telecommunications workplace, acting as an adjunct professor. This working Ph.D. continues to use his well-worn Schaum's as a reference.

Hey what can I say about this book? A great book. The quality is in the name Schums's.

My battered old copy of Spiegel has been a great help to me over the years. Dr. Spiegel provides comprehensive coverage of his topics (for example, unlike other authors of introductory texts he actually provides formulas for assessing skew and kurtosis) and he explains them simply and clearly. I have found his book particularly valuable when I have to use analytical designs or procedures which I do not normally use -- he provides a good overview and gives you a good idea of where you should be looking for further information. Now I've discovered there's an expanded second edition I'm going to order it.

I have used this book as a reference for understanding and solving various types of statistics problems for over ten years. It makes application of statistics concepts to real world problems easy because it contains many good example problems that are solved for the reader. One statistics teacher complemented me on my use of the book because it is very difficult to use the information in other textbooks on problems because they contain very few solved examples. I own a few other statistics books but this one is always the most useful.

This book is about common sense: the way ordinary people have reasoned about the world since the beginning of recorded history. It is a compendium of anecdotes, about anecdotal thinking. I find the insights engrossing, entertaining, and scholarly-if not scientific. This book hopes to rigorously analyze the processes that thinkers have followed throughout recorded history in order to reach rational conclusions. These processes are interesting in their history of use as official rules of thumb, but they are fatally flawed. The fundamental flaw is that the nonscientific processes are not reasoning- they are persuasion, as in rhetoric. Being nonscientific in nature, "The Science of Conjecture" is misnamed, but quite worthwhile to those of us who would like to understand the lawyer and jurors' mind.

This is the sort of book whose indispensability creeps up on you: you start it without any idea that you'll require it to broaden your mind, but it insidiously works its magic. Totally unclassifiable -- it mixes the disciplines of history, mathematics, philosophy and jurisprudence -- it also happens to be a rivettingly lucid read, notwithstanding the outwardly abstruse nature of its materials.

If you read "Sophies World" by Jostein Gaardner and wanted something with more bite, this book is it. It's one of the few truly intellectual books I've read without being academic or boring. I had no idea how much we take certain things in our 21st century thinking for granted. One example is juries and innocence until proven guilty. The book is a marvelous history of legal and ethical thinking and how we came to civilized methods to deal with charges of guilt. It makes me aware of the manipulative power of different styles of logical arguments. Buts it's not only about law. The author explains why Islam is fundamental (God can't be wrong) so why bother considering pros and cons of situations. Christianity was lucky to have the reformation and counter-reformation to challenge why there is probability/chance or unknowing. There are great sections on scientific theory - reasoning for hard sciences like physics and astronomy. Why cannot astrology be a science? Because there are no hard rules; too much depends on the art or experience of interpreters who "explain" exceptions to rules, because so many situations don't follow their rules. The sections on soft science describe biology and medicine, and the impact of clinical trials. How did we arrive at "scientific thinking" to establish proofs? Its all here. I'm not into mathematics and the title sounded so boring to me - mathematics and before the 16th century ie Pascal. If ever there was a case for "don't judge a book by its cover" this is it. Its solid reading, but it is also deeply satisfying and fascinating in understanding a little bit more about how and why we think like do in the 21st century. As an aside the author is also a Latin scholar who translates many texts, correcting false interpretations. But he does it in subtle ways; nothing show-off. James Franklin dazzles us with his humility one moment and superb, accessible writing on complicated subjects the next moment. I never knew that "like" and "probably" were introduced from Greek. Medieval Europeans did not have sophisticated languages that included "like" or "probably" but with medieval enlightment they were introduced. What an impact these two words had. The author corrects cultural misthinking of how poor medieval thinking was. It was an explosion of brilliance in virtually a person's lifetime from 1150-1200. The Renaissance was mild in comparison. This book touches and explains our human development of consciousness and thinking in so many fields eg law, medicine, science, ethics. The author draws on Ancient Greek texts, Roman texts, the Talmud, Jewish philosophers, Islamic philosophers, Christian theologians and even Sanskrit writings. The subjects discussed heavily affect my daily life and thinking. Understanding a little bit of what we take for granted, makes me reconsider glib, slick arguments I'm confronted with in newspapers and television every day. If you buy the book, it's a great read over 1-2 months that can be savored and sipped like a great wine.

A good, albeit highly idiosyncratic, guide to Statistical Learning. The highly personal account of the theory is both the strong point and the drawback of the treatise. On one side, Vapnick never loses sight of the big picture, and gives illuminating insights and formulations of the "basic problems" (as he calls them), that are not found in any other book. The lack of proofs and the slightly erratic organization of the topic make for a brisk, enjoyable reading. On the minus side, the choice of the topics is very biased. In this respect, the book is a self-congratulatory tribute by the author to himself: it appears that the foundations of statistical learning were single-handedly laid by him and his collaborators. This is not really the case. Consistency of the Empircal Risk Measure is rather trivial from the viewpoint of a personal trained in asymptotic statistics, and interval estimators for finite data sets are the subject of much advanced statistical literature. Finally, SVMs and neural nets are just a part of the story, and probably not the most interesting.
In a nutshell, what Vapnick shows, he shows very well, and is able to provide the "why" of things as no one else. What he doesn't show... you'll have to find somewhere else (the recent Book of Friedman Hastie & Tibs is an excellent starting point).
A last remark. The book is rich in grammatical errors and typos. They could have been corrected in the second edition, but do not detract from the book's readability.

This is a very readable book by an authority on this subject. The book starts with the statistical learning theory, pioneered by the author and co-worker's work, and gradually leads to the path of discovery of support vector machines. An excellent and distinctive property of support vector machines is that they are robust to small data perturbation and have good generalization ability with function complexity being controlled by VC dimension. The treatment of nonlinear kernel classification and regression is given for the first time in the first edition. The 2nd edition includes significant updates including a separate chapter on support vector regression as well as a section on logistic regression using the support vector approach. Most computations involved in this book can be implemented using a quadratic programming package. The connections of support vector machines to traditional statistical modeling such as kernel density and regression and model selection are also discussed. Thus, this book will be an excellent starting point for learning support vector machines.

Vapnik and collaborators have developed the field of statistical learning theory underlying recent advances in machine learning and artificial intelligence (e.g. support vector machines). This book almost accomplishes the formidable task of comprehensibly describing the essential ideas of learning theory to non-statisticians. It contains ample theorems but almost no proofs.

While probably not the worst book I've had to learn from, it seems like the people who wrote this book subscribe to the same philosophy of teaching that my professor uses, which is namely to keep closely to theory and not use too many examples. At least not any fully worked out examples, and hardly any with actual numbers. As a result, it's difficult to learn and easy to get lost.

At the same time, I've looked in some other books and they're not much better. Woe is the student who has to learn solely from such an obtuse book (woe is me)

I read this book several times and I can say that it is the best statistical&probability book for engineers or for computer scientist. My major is Image Processing (DSP, DIP, DVP) and reading this book helps me to increase my professional knowlege and rise my skills. I sincerelly recommend this book for any non-math major person. Now this book becomes desktop book for me like "Numercal Recipes in C" for any algorithm developer.

As a professor in Electrical Engineering, I highly rate this book, describing it as the best text for an introductory course on prabability theory, statistics, random signals, and the analysis of systems with random signals as inputs. If you teach from this book, you can't go wrong!

Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.

The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.

Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.

A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).

This is clearly an advanced text that mathematicians and PhD students in statistics would find valuable. It is not for an engineer or novice statistican in industry (like myself) who has to come up with an accurate regression model with quantitative and qualitative data in a short period of time. My rating is four stars: buy this book only if you have the advanced statistical training to understand it, otherwise buy a simpler book if you want to get a basic understanding of the subject.

This text does a five star job of what the title advertises. The book could be used for a one year graduate course in applied linear models. The writing is excellent, and topics very up to date. This is for graduate students with a good foundation in mathematical statistics and applied statistics. Very good integration with modern statistical packages.