Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

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(More customer reviews)This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)Note, this is a review of the first edition.
Overview
This book was the textbook used at the University of Wisconsin-Madison for the graduate course in Bayesian Decision and Control I during the fall of 2001 and 2002. It strikes a good balance between theory and practical example, making it ideal for a first course in Bayesian theory at an intermediate-advanced graduate level. Its emphasis is on Bayesian modeling and to some degree computation.
Prerequisites
While no Bayesian theory is assumed, it is assumed that the reader has a background in mathematical statistics, probability and continuous multi-variate distributions at a beginning or intermediate graduate level. The mathematics used in the book is basic probability and statistics, elementary calculus and linear algebra.
Intended audience
This book is primarily for graduate students, statisticians and applied researchers who wish to learn Bayesian methods as opposed to the more classical frequentist methods.
Material covered
It covers the fundamentals starting from first principles, single-parameter models, multi-parameter models, large sample inference, hierarchical models, model checking and sensitivity analysis (model checking and sensitivity analysis are especially well covered), study design, regression models, generalized linear models, mixture models and models for missing data. In addition it covers posterior simulation and integration using rejection sampling and importance sampling. There is one chapter on Markov chain Monte Carlo simulation (MCMC) covering the generalized Metropolis algorithm and the Gibbs sampler.
Over 38 models are covered, 33 detailed examples from a wide range of fields (especially biostatistics). Each of the 18 chapter has a bibliographic note at the end. There are two appendixes: A) a very helpful list of standard probability distributions and B) outline of proofs of asymptotic theorems.
Sixteen of the 18 chapters end with a set of exercises that range from easy to quite difficult. Most of the students in my fall 2001 class used the statistical language R to do the exercises.
The book's emphasis is on applied Bayesian analysis. There are no heavy advanced proofs in the book. While the proofs of the basic algorithms are covered there are no algorithms written in pseudo code...Additional books of related interest
1) Statistical Decision Theory and Bayesian Analysis, James Berger, second edition. Emphasis on decision theory and more difficult to follow than Gelman's book. Covers empirical and hierarchical Bayes analysis. More philosophical challenging than Gelman's book.
2) Monte Carlo Statistical Methods, Robert and Casella. Very mathematically oriented book. Does a good job of covering MCMC.
3) Monte Carlo Methods in Bayesian Computation, Ming-Hui Chen, Qi-Man Shao, Joseph George Ibrahim. An enormous number of algorithms related to MCMC not covered elsewhere. If you need MCMC and need an algorithm to implement MCMC this is the book to read.
4) Monte Carlo Strategies in Scientific Computing, Jun S. Liu. Covers a wide range of scientific disciplines and how Monte Carlo methods can be used to solve real world problems. Includes hot topics such as bioinformatics. Very concise. Well written, but requires effort to understand as so many different topics are covered. This book is my most often borrowed book on Monte Carlo methods. Jun S. Liu is a big gun at Harvard.
5) Probabilistic Networks and Expert Systems. Cowell, Dawid, Lauritzen, Spiegelhalter. Covers the theory and methodology of building Bayesian networks (probabilistic networks).

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Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:
Stronger focus on MCMC
Revision of the computational advice in Part III
New chapters on nonlinear models and decision analysis
Several additional applied examples from the authors' recent research
Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more
Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

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Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics) Review

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(More customer reviews)I have used 'Bayesian Core' to teach Bayesian statistics to a class of Masters students majoring in finance, statistics or business with an undergraduate mathematics/statistics background and found it to be quite a good book for this purpose. I would have to disagree though with the previous reviewer (A.L.H Mayne) and suggest that this book could also be good for a practitioner, provided they have some prior statistical and mathematical understanding. A limitation for self guided study is the absence of solutions to exercises or hints necessary to ensure understanding for some of the exercises. I would also have to disagree with the previous reviewer over what is or what is not discussed in the book! The statement "conjugacy is mentioned in exercise 2.10 on page 22 with no discussion" is simply not true, the entire page preceding this exercise discusses conjugate priors in particular and they are subsequently used and outlined in the following chapters. There is also considerable attention paid in the book to the use of improper priors, in particular with respect to the implications of using improper priors for the estimation of Bayes factors (Chapter 3). A more thorough description of Jeffrey's Lindley paradox could be provided but a more complete discussion of this seems outside the scope of the book. Similarly, outlining the "historical antecedents ..." and more "...theory" about Jeffrey's prior than what is already provided, while interesting and important in its own right, is not necessarily mandatory reading for the student or practitioner who seeks a practical introduction to the Bayesian approach. To highlight "The implementation of the Monte Carlo method is straightforward" without adding the next few words used in the book "at least on a formal basis" seems terribly insincere and pedantic.
What the book is? The book presents a Bayesian approach to the analysis of topics commonly analysed in statistics designed to allow the reader to quickly grasp the essential elements of Bayesian principles and to put this into practice with examples using R code (simple computing syntax) provided on the website. There are surely limitations of this approach (albeit also acknowledged by the authors!) namely a less than full treatment of topics and of theoretical derivations for approaches. Some of the exercises are also difficult and these exercises could well do with hints being provided or a short statistical annotation.



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This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.

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Handbook of Parallel Computing and Statistics (Statistics: A Series of Textbooks and Monographs) Review

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(More customer reviews)It came as somewhat of a surprise to the industry that coupling together several PC's enabled the construction of what was in effect a supercomputer at a small fraction of the cost.
What began twenty or so years ago has now influenced the design of CPU's and the intereconnection 'LANs' that facilitate the transfer of data between the processors. And this clearly hasn't stopped. The AMD Opteron CPU's and Intel's PCI-Express are simply the latest innovations in silicon, and more is coming.
From a system architecture standpoint, we have (and the book discusses) clusters, Grids, and distributed processor systems -- all of which are fairly loosely defined with plenty of room for very good discussions over several beer.
What this book brings is an excellent introduction into the state of the art in parallel computers as it exists today. As is often the case with books that are pushing the state of the art, it is written by a large numnber of experts and edited together. Each chapter covers a particular area in depth from the design of the hardware to the languages (primarily Fortran and Java), to the solution of a series of common problems that are frequent in several different application areas.
This book is an excellent summary of parallel computing as it exists today. It would be of particular help to the person responsible for writing the proposal for an organization to buy/build one. The book is probably a bit too advanced for a course at an undergraduate level, but would be excellent for first year graduate students in a wide variety of fields from computer science to bio-informatics, data mining, cryptography or any number of other fields requiring heavy duty computation.

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Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts, such as grid computing and massively parallel supercomputers. The Handbook of Parallel Computing and Statistics systematically applies the principles of parallel computing for solving increasingly complex problems in statistics research.
This unique reference weaves together the principles and theoretical models of parallel computing with the design, analysis, and application of algorithms for solving statistical problems. After a brief introduction to parallel computing, the book explores the architecture, programming, and computational aspects of parallel processing. Focus then turns to optimization methods followed by statistical applications. These applications include algorithms for predictive modeling, adaptive design, real-time estimation of higher-order moments and cumulants, data mining, econometrics, and Bayesian computation. Expert contributors summarize recent results and explore new directions in these areas.
Its intricate combination of theory and practical applications makes the Handbook of Parallel Computing and Statistics an ideal companion for helping solve the abundance of computation-intensive statistical problems arising in a variety of fields.

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Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics) Review

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(More customer reviews)I will disagree with Eric on this book being a must-have for any "applied quantitative" statistics or marketing Ph.D. student, and call it a must-see for people interested in Bayesian discrete-choice modeling. The five case studies are all examples of marketing research, but are relevant to a much broader audience - consider, for example, "scale usage heterogeneity", affecting analysis of rating-scale responses. The case-study chapters are the book's forte, but it also offers a proper and rigorous introduction to Bayesian modeling, including the expected topics such as simulation (MCMC, Gibbs sampler, etc.) and linear regression, but also chapters on HLM, endogeneity, and model selection. The authors discuss doing Bayesian computation with R package bayesm, but regrettably relegate R material to appendices instead of integrating it into the main narrative and making implementation transparent and reproducible.

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The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.
Written by the leading experts in the field, this unique book:
Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
Provides a self-contained introduction to Bayesian methods.
Includes case studies drawn from the authors' recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book's website hosts datasets and R code for the case studies.
Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.

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Bayesian Statistical Modelling (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Congdon presents a very nice and modern treatment of Bayesian methods and models emphasizing implementation using BUGS or WINBUGS. The book covers Bayesian models for regression including linear, log-linear, robust and nonparametric regression. Covers association and classification, mixture models, latent variables, problems of missing data, survival analysis, hierarchical models for pooling information, time series and other correlated data methods (e.g. spatial processes), multivariate analysis, growth curves and model assessment criteria.
The book is loaded with techniques and applications covering a wide variety of topics with reasonable depth.
It also has a very large bibliography with many very relevant and useful references. But there is also a negative side to the bibliography. It was not carefully proofread and there are some annoyances as you will see the same reference listed two, three or more times in the bibliography. Also for such a nice reference text it should have included an author index as well as an ordinary index.
Gibbs sampling is one of the primary estimation techniques in the book but the details are put off until section 10.1 where we get a nice introduction to Gibbs sampling and also the Metropolis algorithm with several excellent references.
This is a good book to start implementing Bayesian methods through the MCMC technique. It contains mostly medical applications which is a nice feature for biostatisticians.


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Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.
Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.
The second edition:
Provides an integrated presentation of theory, examples, applications and computer algorithms.
Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
Provides exercises designed to help reinforce the reader's knowledge and a supplementary website containing data sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.
Praise for the First Edition:
"It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains." – ISI - Short Book Reviews
"This is an excellent introductory book on Bayesian modelling techniques and data analysis" – Biometrics
"The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods." – Journal of Mathematical Psychology

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Bayes and Empirical Bayes Methods for Data Analysis, Second Edition Review

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(More customer reviews)This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.

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Multidimensional Item Response Theory (Statistics for Social and Behavioral Sciences) Review

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(More customer reviews)The MIRT sub-field of psychometrics has for years labored in obscurity, due in no small part to the inability of its practitioners and students to understand each other and master each other's algorithms and models. Reckase, one of the field's leaders, takes a bold step in correcting the situation. Excellently researched, clearly written, logically presented, fair and balanced, Reckase summarizes the foundations of probabilistic unidimensional models and shows how they generalize, so that persons (test examinees) and items (test questions) can be represented as points (vectors) floating around in a multidimensional space.
This is not a book for the field practitioner or the casual researcher. It does not skip over the math, and the math is hard-core. Nonetheless, it is surprisingly readable. The reader will be pleased to find himself following the gist of Reckase's explanations without difficulty, even when the mathematical details are too much.
To appreciate this work, it is important to know why MIRT is important. Unfortunately, Reckase never tells us. We understand that MIRT is motivated by the fact that items and tests are complex, that they embody multiple dimensions, that therefore a multidimensional model is necessary. This hardly touches the surface. As the fantastic drama of the Netflix contest revealed (a recently resolved contest to win $1 m. for best predicting movie ratings), we live in a world of psychological profiling and prediction, a world populated by weird and incredible mathematical models that touch on every aspect of life -- from selecting food at Safeway, to renting movies, to profiling terrorists, to guiding teacher instructional decisions, to training computers to read and understand text and recognize the spoken word. None of that is in this book. The great divide between educational psychometrics and "data mining" or "knowledge discovery" has yet to be crossed. MIRT is the subfield within educational psychometrics that will ultimately bridge that divide.
On the theory side, Reckase does not conceal his differences with the "Rasch School" of psychometrics (of which I am a member) regarding the purpose of educational measurement and modeling, though he is obviously well-versed in Rasch models and presents them well, including their MIRT flavors. He sees the purpose of a model to be "descriptive" (to describe the data closely), whereas Rasch theorists see the purpose of a model to be "prescriptive" (to prescribe the conditions under which data yield true measures, i.e., measures that are most likely to reproduce across datasets regardless of person and item samples). The models that Reckase speaks about with the confidence of personal knowledge are "descriptive" in this sense.
Due perhaps to his preference for descriptive models, I found there were certain questions that Reckase did not seem to spend time on, questions that are huge for me:
1. How well do MIRT models handle small sample sizes?
2. How do they handle missing data, whether randomly or non-randomly missing?
3. To what degree are the person and item parameters invariant across samples? Can I cherry-pick my samples and get different parameters?
These are the sorts of questions Rasch people are always asking and where the Rasch model, properly used, has much to offer.
I also found myself looking in vain for discussion of Rasch's "specific objectivity" property as relates to MIRT, often called the "invariance" property. I learned that Reckase means something else entirely by the same word. In the Rasch world, "invariance" means that item and person parameters, and the resulting response probabilities, are invariant across samples, that persons will obtain the same relative measures regardless of what items they are administered so long as the items embody the same dimension. For Reckase, "invariance" means that the origin and orientation of the coordinate system can be moved without affecting the response probabilities. It's got nothing to do with samples. So, in the end, I still don't know what, if any, invariance properties the various MIRT models discussed in the book possess, defining "invariance" in the Rasch sense as invariance across person and item samples.

But those are my problems, not Reckase's. This book is a significant step forward in the maturation of an extraordinarily important, but little known, field.

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First thorough treatment of multidimensional item response theoryDescription of methods is supported by numerous practical examplesDescribes procedures for multidimensional computerized adaptive testing

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Risk Assessment in Geotechnical Engineering Review

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(More customer reviews)Two years ago when I started my masters in structural engineering, I had to learn about random field theory. The books that I found, considered the "must reads" for random field theory, might as well have been in Chinese (I don't understand Chinese - just for the record). It was extremely painful and frustrating trying to understand the completely not clear or easy to understand concepts - as they were presented in those books. Upon searching the internet, I found some papers written by Gordon Fenton. His description of random field theory and his work in general, was like an oasis in the desert. This is one very rare person who can explain complicated concepts so that they are easy to understand and apply. So when I found out he was publishing a book, I thought, this is a must have. And I haven't been disappointed. The first section on probability theory is without a doubt, the best explanation of the theory I have seen, with simple examples to help me concrete my understanding of the theory being presented. Therefore, this is the first textbook that I would recommend for people trying to understand the concepts of probability theory. Similarly, all other sections in the book are equally easy to understand and can make even me look intelligent - as he enables me to really understand the concepts being presented, because of his fantastic ability to communicate ' I like in particular, how he explains the practical reasons for why the theory can be simplified or assumptions can be made. Most boffin writers of textbooks don't explain "the obvious" which is not so obvious to the novice person reading their books. Sometimes I think that they don't realise that not everybody has done the mathematics degree required to understand some of the intricacies, which if not clearly explained, can stump the reader for days until they find out why they have assumed this or that. That's where Gordon Fenton is a real a gem. He doesn't leave you scratching your head, scrambling for other textbooks to fill in the gaps before you can continue with his explanations. So I highly recommend this book to anybody trying to understand probability theory, random field theory, estimation, reliability, Monte Carlo simulation and all other topics he covers in his book.

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Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series) Review

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(More customer reviews)In the pharmaceutical industry adaptive designs are currently the rage because of their many potential advantages due to their flexibility. It allows you to stop early for efficacy or futility. It can do drug dose selection more easily and may have patients on inferior treatment for smaller amounts of time. There have already been four or five books published from the frequentist point of view. This is the first serious text on adaptive designs using the Bayesian approach. Pharmaceutical companies including Johnson and Johnson, Eli Lilly, Pfizer, Merck, Novartis, Novo Nordisk, Millennium, AMAG and GlaxoSmithKline have all been successful at running adaptive trials. Merck for example has already completed more than 40 adaptive design trials. Such trials can be done in phase II, phase III or a combining of phases II and III in a single adaptive trial. Merck claims to have completed over 40 adaptive trials. The M D Anderson Medical Center at UT Houston runs hundreds of adaptive trials (all as far as I know using the Bayesian methodology). Don Berry runs the biostatistics group at M D Anderson and he and his son scott own a consulting group that helps companies run Bayesian adaptive designs. Eli Lilly has been one of their clients on a drug trial and Biosense Webster, a J& J company, used them for a Bayesian trial on one of their ablation catheters. Scott Berry isone of the authors of this book and a lot of the book is devoted to work of Berry first at Duke and then later at M D Anderson and Berry Consultants.
Adaptive designs have logistic problems but companies have been able to overcome the problems motivated by the overall time and money saving benefits. All types of studies are illustrated from phase I through phase III and the examples are real and practical. Even when taking the Bayesian approach issues of frequentist properties for the designs comes up. Missing data, multiple testing, type I error and power of the test conditional and unconidtional are important when the frequentist approach is applied. The authors admit that both frequentist and Bayesian properties for a design are important and can be evaluated through simulation.
Although adaptive designs can be implemented effectively using either the Bayesian or the frequentist approaches. But Bayesian trials are a little more natural and simpler. This is the right book to get if you are interested in Bayesian methods.

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Probability Theory and Statistical Inference: Econometric Modeling with Observational Data Review

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(More customer reviews)This book is absolutely remarkable.
It presents the material traditionally taught in the second-year statistics (but actually goes well beyond, e.g. stochastic processes) and will be of interest to all people interested to (re-)learn statistics well, either undergraduates, or advanced students of any level. Professors should also read it maybe use it in class. Students will thank them.
The author took more than 14 years to polish it, and I would bet that scholars of pedagogy will put this book as an example of the highest possible level of their discipline. I would also bet that this book will have a long and brilliant career in statistical education. On gets the feeling that the author gave the same care and energy to the elaboration of this book as people commonly give to research.
The author is also a man with a mission. In his preface, one can read with pleasure and disbelief a passionate attack on the dumbing-down of undergraduate education in Europe and America. Having taught undergrads in commerce with the "predigested pap" he is talking about, I can really relate to the frustration of the author. There is no dilution of material or dumbing down here: all the ugly details are given, which makes that book not only a pedagogical tool but also a great reference.
There is no book on the market that is so polished in both presentation and discussion, that exposes intermediate stats at such an intelligent, comprehensive level, and finally that uses the historical development to project such clarity on the actual state of the science. I would say the closest competitor to this book is the great volume "Intermediate Statistics" by Dale Poirier, which has more econometrics and which might be a bit more comprehensive on the bayesian side, but the main focus of this one is on UNDERSTANDING and SYNTHESIZING. By the way, the focus of this book is statistics, not econometrics, despite the fact that the author has written extensively in econometrics.
I wish I would be an undergrad again and re-learn statistics with this book. Nevertheless, readers of all levels will learn something from it.
Ah, and the price is right. Value for your money!

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This major new textbook is intended for students taking introductory courses in probability theory and statistical inference. The primary objective of this book is to establish the framework for the empirical modeling of observational (nonexperimental) data. The text is extremely student friendly, with pathways designed for semester usage, and although aimed primarily at students at second-year undergraduate level and above studying econometrics and economics, Probability Theory and Statistical Inference will also be useful for students in other disciplines that make extensive use of observational data, including finance, biology, sociology and psychology.

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Regression Modeling Strategies Review

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(More customer reviews)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).


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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

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Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling Review

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(More customer reviews)This is the most succinct, clear mathematics book I have ever own. Unlike many mathematics books whose mathematical derivations usually have several missing yet important steps make people scratching their heads, this books is not one of them. All the derivations are very detailed along with great explanations and numerical examples. It is a rare gem in mathematical literature and I salute Prof. Stewart for his great achievement.

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Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling. The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a fundamental role. The textbook is relevant to a wide variety of fields, including computer science, engineering, operations research, statistics, and mathematics.

The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers. Discrete and continuous-time Markov chains are analyzed from a theoretical and computational point of view. Topics include the Chapman-Kolmogorov equations; irreducibility; the potential, fundamental, and reachability matrices; random walk problems; reversibility; renewal processes; and the numerical computation of stationary and transient distributions. The M/M/1 queue and its extensions to more general birth-death processes are analyzed in detail, as are queues with phase-type arrival and service processes. The M/G/1 and G/M/1 queues are solved using embedded Markov chains; the busy period, residual service time, and priority scheduling are treated. Open and closed queueing networks are analyzed. The final part of the book addresses the mathematical basis of simulation.

Each chapter of the textbook concludes with an extensive set of exercises. An instructor's solution manual, in which all exercises are completely worked out, is also available (to professors only).

Numerous examples illuminate the mathematical theories
Carefully detailed explanations of mathematical derivations guarantee a valuable pedagogical approach
Each chapter concludes with an extensive set of exercises

Professors: A supplementary Solutions Manual is available for this book. It is restricted to teachers using the text in courses. For information on how to obtain a copy, refer to: http://press.princeton.edu/class_use/solutions.html

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Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (Chapman & Hall/CRC Interdisciplinary Statistics) Review

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(More customer reviews)This book provides interesting elements about quantitative methods in epidemiology for master students or researchers. it is quite easy to read when you have some basic background in statistics. Nethetheless, the quality of the fonts is not the best, and there are some surprising typing errors even in early pages as the one about "list of tables". Plenty of relevant references on papers and useful softwares.

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Professor Gelman has edited a book containing 29 articles dealing primarily with real applications of Bayesian methods for causal inference and the treatment of incomplete data.
It contains a collection of the best work in applied statistics by prominent statisticians. In addition to learning the wide variety of problems that have been solved using the Bayesian approach (particularly in the medical field) the reader can learn and appreciate the power and ease of interpretation of Bayesian results.

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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
Comprehensive coverage of an imporant area for both research and applications.
Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
Includes a number of applications from the social and health sciences.
Edited and authored by highly respected researchers in the area.

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Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)I've bought several spatial statistics books over the years and found they generally fall into one of two categories; oversimplified or cover-to-cover matrix notation, neither of which is very useful for my research. However, this book is "just right," bridging these two extremes. It briefly covers the basics of both point and areal analysis, then gives the reader the tools for more advanced (i.e., realistic) analysis. They devote a chapter to Bayesian basics, which is needed for the last 4 or 5 chapters. The last few chapters weave together a detailed discussion on a variety of hierarchical models and current published results. Most importantly this book offers quite a bit of the necessary R and Winbugs code. Although many of their examples are from the public health world, the techniques and code are easily adapted to natural resource data - my personal focus.

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Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

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Bayesian Modeling Using WinBUGS (Wiley Series in Computational Statistics) Review

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(More customer reviews)I try to read at least a couple of statistics books every year and this was one of them for 2009. So far I have been really impressed. If you want a complete introduction to Bayesian statistics, then buy this book. You will find a balanced blend of theory, applications, and the use of the WinBUGS software package all under one roof. The book's treatment of models for count data is notable. Ntzoufras has a nice way of expressing himself that makes the reading move along. I would have no compunction at all about using this book to teach a M.S.-level course for statistics majors. If you are an ecologist, say, then you should probably have both a probability and mathematical statistics course under your belt to fully absorb all that is going on.

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A hands-on introduction to the principles of Bayesian modeling using WinBUGS
Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including:

Markov Chain Monte Carlo algorithms in Bayesian inference

Generalized linear models

Bayesian hierarchical models

Predictive distribution and model checking

Bayesian model and variable evaluation

Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

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