Book Description

Called the "bible of applied statistics," the first two editions of the Handbook of Parametric and Nonparametric Statistical Procedures were unsurpassed in accessibility, practicality, and scope. Now author David Sheskin has gone several steps further and added even more tests, more examples, and more background information-more than 200 pages of new material.The Third Edition provides unparalleled, up-to-date coverage of over 130 parametric and nonparametric statistical procedures as well as many practical and theoretical issues relevant to statistical analysis. If you need to…·Decide what method of analysis to use·Use a particular test for the first time·Distinguish acceptable from unacceptable research·Interpret and better understand the results of pubished studies…the Handbook of Parametric and Nonparametric Statistical Procedures will help you get the job done. ... Read more

Reviews (5)

Useful reference book
Got it as a reference book to help me understand statistical tests. Havent used it much in my daily work, but its very comforting to know that its there. Quite readable, and extremely bulky to handle. Guess this will go on to be a classic in the field ..sad reality is that most of the methods discussed are already in programs like Minitab & S-Plus, so you can do most of the analysis without slogging through the theory.

Comprehensive, valuable reference
This is an excellent, exceptionally comprehensive, and well-structured reference on a wide range of inferential tests and measures of association, which is ideal for the applied scientist. After a few useful introductory chapters that provide definitions and outline the main concepts involved in inferential statistics, the various tests are covered chapter by chapter. Each chapter contains sections describing:
1. the hypotheses evaluated and relevant background information;
2. examples of the kind of problems that can be addressed using the test
3. the null and alternative hypotheses;
4. computation (including meticulously worked out examples so the reader can follow the precise workings);
5. an extensive set of notes describing the interpretation of the results, the assumptions of the test, the robustness of the test to violations of those assumptions, and comparisons with alternative tests;
6. a set of references.
A set of decision tables are provided to assist the user in selecting the appropriate test, and there are additional extensive discussions in the various chapters to assist further if required. The book also includes an extensive list of look-up tables for significance testing. An additional valuable feature of the book is that in the chapter on the nonparametric test for a difference in medians of two populations (Mann-Whitney U-test) there is included discussions about permutation and randomization tests.

The target audience of the book is the practioner rather than the theoretician. The book aims to assist in the selection of an appropriate test and the interpretation of the test results rather than on a theoretical discussion of the test. The text is exceptionally clearly written, and is highly accessible to non-experts in statistics. There is a minimum of equations, which are supplied only where necessary. While it is not that hard to find a few editorial omissions, the book does seem to have been edited carefully, and I have as yet stumbled across only trivial errors. If I were forced to find any criticism, about the only thing I could say is that at the top of the page the chapter headings are listed purely in terms of test number without listing the test name, which sometimes makes it a little harder to find the test of interest.

I make absolutely no hesitation in recommending this book to anyone who makes use of inferential statistics.

Backbone for statistical analysis
Excellent book for handling all common and uncommon stats tests. Plenty of examples for application, an overview for each. This book is good for those that have had stats and need a reference guide on them later. I would not recommend this for anyone that has not actaully formally studied higher end stats.

Superb handbook
"One stop shopping" for information on statistical testing

A great aid for understanding the use of statistical tests.
From a student's point of view, this book contains numerous aids to assist in understanding the application of many statistical tests. For example, the decision charts on pages 27-30 help to determine what tests are applicable for analysis given the type of data the researcher plans to obtain in the study. I might decide that the use of the Kruskal-Wallis is appropriate. The chart refers me to Test 17, which is on page 396. I can go to that page, and get a complete plain-language explanation of the test and what it measures. Examples are provided for each test to determine appropriateness, and this section is followed by an example with computations. A complete analysis of the data and a discussion of the test ensues. All of the necessary tables for computation are included in appendices. I am a complete novice at statistics, and this book has helped me to understand material in classroom lectures. ... Read more

Book Description

The market-leading textbook for the course, Winston's OPERATIONS RESEARCH owes much of its success to its practical orientation and consistent emphasis on model formulation and model building. It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis. ... Read more

Reviews (13)

Statistics Without Angst, Ennui & Pure Freaking Out!
I credit this book with helping me get through my doctoral stats course. Yes, I am Severely Mathematically Impaired and was terrified at getting through the required stats. Never mind that everyone said math wasn't part of it -- it was all absolute Greek to me. Then came the Rowntree Recommendation -- and the basic concepts in genuine basic English. No pointyheaded stuff. Clear, concise -- the perfect read PRIOR to the assigned text chapters (which were awful beyond comprehension!). The Stats experience was hideous -- until one day, verrrrry late in the semester, when all of a sudden, it all made sense. (Great prof, but I still credit this book.) I write this 17 years after the fact -- because I just recommended this book AGAIN book for a similarly scared person going for the MBA -- just one of several rec's over the years but decided to check and see if it's still in print. I have a paperback and was astounded at the price, but, quite frankly, anyone who is really angst-ridden and/or needs comfort in navigating the other-world of stats should get this book! [As necessary, I STILL refer to it -- I mean, who can remember all the nuances of this stuff? When I'm confronted now with interpretation, I do a quick review to regain my sense of content reality.] There may be a zillion newer books out there, but this one is really, really good for the Stats Scared/Math Impaired!

Excellent Primer on Statistical Methods for Amateurs
Derek Rowntree's Statistics Withuot Tears is an excellent primer on concepts in statistics, written in very easy-to-understand language. It has just enough mathematics in it to be useful as a reference without being opaque.

Several books I have read written for "non-mathematicians" have not fit the bill, but this book explains statistics in a way that an average person can understand.

A friend of mine says that he recommends this book to friends when they are having difficulty in college statistics courses; it is an excellent tool for explaining concepts in a subject that mathematics instructors sometimes do not explain well.

Contents of the Book
(The "Search Inside the Book" feature was not available for this book at the time this review was posted)

One of the things that help me decide whether or not to buy a book is the table of contents. I hope the information below is useful.

Introduction.
How to use this book.

1. Statistical inquiry.
Making sense of experience.
What is statistics?
Descriptive and inferential statistics.
Collecting a sample.

2. Describing our sample.
Statistical variables.
Error, accuracy and approximations.

3. Summarizing our data.
Tables and diagrams.
Central tendency (averages).
Measures of dispersion.

4. The shape of a distribution.
Skewed distributions.
Introducing the normal distribution.
Proportions under the normal curve.
Comparing values.

5. From sample to population.
Estimates and inferences.
The logic of sampling.
A distribution of sample-means.
Estimating the population-mean.
Estimating other parameters.

6. Comparing samples.
From the same or different populations?
Significance testing.
The significance of significance.
Comparing dispersions.
Non-parametric methods.

7. Further matters of significance.
One- versus two-tailed tests.
z-tests and t-tests.
Comparing several means.
Comparing proportions.

8. Analysing relationships.
Paired values.
Three kinds of correlation.
The strength of a correlation.
The significance of a correlation coefficient.
Interpreting a correlation coefficient.
Prediction and regression.

Postscript.
Review.
Caution.

Bibliography.

Index.

Small and Expensive? - Maybe Not.
This book is true to its title. It IS a primer for non-mathematicians. It teaches extremely useful concepts in a concise and simple way with as little math as possible. A small and easy to read book - you will want to take it with you and read it on the bus, plane, subway, etc.
The only drawback is the price - however, it may be not so expensive if it saves you several hours that would be otherwise spent trying to read other, less readable, books. How much is your time worth to you?

A great leap in understanding statistics but at a price
I just read this book, although I originally purchased the it a number of years ago. I'm afraid my "math anxiety" kept me from reading it although my desire to conquer this stuff remained. Finally, working on a PhD I was again faced with quantitative methods which set me to the task again.

Reading this text non-stop was relatively painless and exceedingly enlightening. Rowntree at least 95% of the material very clear and understandable and avoided too many side trips to the theoretical. Suddenly, the information I was confronted with in class began to make sense given his "down-to-earth" explanations. However, he doesn't stray far from the theoretical and mathematical roots of the science of probability.

He takes the reader from the point of understanding we all do statistical analysis informally regardless of any coursework. From there, he proceeds through the fundamentals until reaching the essence of correlation and regression.

My first of two critiques would be that he leaves out some basic symbols in favor of simplicity. Yet these symbols could easily have remained in. This would have aided the transition from understanding to classroom work. Second, the price for the volume is extraordinary. The price is a clear disincentive. Although the knowledge is easily worth [more money], I'm afraid this reduces the number of readers for an extremely valuable primer. ... Read more

Book Description

Using high-quality, real-world case studies and examples, this introduction to mathematical statistics shows how to use statistical methods and when to use them. This book can be used as a brief introduction to design of experiments. This successful, calculus-based book of probability and statistics, was one of the first to make real-world applications an integral part of motivating discussion. The number of problem sets has increased in all sections. Some sections include almost 50% new problems, while the most popular case studies remain.For anyone needing to develop proficiency with Mathematical Statistics. ... Read more

Reviews (9)

Excellent introduction to statistics...
This book manages to stay focused on the main ideas all the way through. It uses no more math than what is necessary to derive the proofs of most theorems (although some are omitted). The main ideas of each chapter is introduced before the details are worked out, and summarized at the end of each chapter. The examples and case-studies are usually interesting (sometimes thought-provoking), instead of solely being based on urns and coloured balls. And the exercises range from trivial to interesting...

In short, this is about as good as a textbook gets...

Great Book
This book is very useful in learning statistics. The text has many applications and problems that are practical. The authors include interesting biographical background on certain mathematicians and topics--this helps the student feel more connnected with the material. I highly recommend buying a copy (especially used--better deal) of this book for class or personal study.

Excellent Introductory Text
This is an excellent introductory text for students who wish to know more about mathematical statistics. The book is ideal for a student with a good solid year of mathematics, and the case studies really make the statistics theory more relevant

Clear exposition of statistical concepts
I found the book to have clear explanations of many statistical concepts. I am a computer science graduate student with no formal background in statistics. I have looked at several statistics and probability theory textbooks and have found this one to be the easiest to understand and clearly written. That said, I am not using this book for a course, so I am not doing practice problems. The small number of problems for each section may be a shortcoming for those who are using this book for a course. Also, the proofs can be a little terse, although I have found this to be the case with other stat books.

Good Intro for the Mathematically Mature
This text is a good introduction to probability theory and mathematical statistics for the mathematically mature. It presumes a working knowledge of multivariate calculus and good facility with symbolic manipulation. The major shortcoming, though, is the small amount of sample problems and exercises after each section. As this book has not been revised since the mid-1980's, perhaps the authors will take this into account for the next edition (I await). Two particularly nice features of this book are the historical backgrounds of probability theory and the dry, dark humor that the authors interject. These make the book a bit more entertaining.

As for the reviewer below, from Wharton: I give you credit for taking STAT-430 and not 101, but do you really believe the book is quite that bad? ... Read more

Book Description

While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data.
This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field
Requires only minimal background in public health and only some knowledge of statistics through multiple regression
Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure
Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks")
Exercises based on data analyses reinforce concepts ... Read more

Book Description

This example book contains C++ source programs that exercise and demonstrate all of the subroutines, procedures, and functions in Numerical Recipes in C++. The book will be a valuable aid to readers wishing to incorporate Numerical Recipes procedures and subroutines into larger programs and to conduct simple validation tests. Each example program contains comments and is prefaced by a short description of what it does and of which Numerical Recipes routines it exercises.In cases where the demonstration programs require input data, those data are also supplied. In some cases, sample output is also shown. ... Read more

Book Description

This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called ‘prior ignorance. The work is written from the authorss committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics ... Read more

Reviews (4)

The Standard First Text To Begin Studying Bayesian Methods
This is an extremely nice introduction to Bayesian statistical methods. It takes you from the very basics - even who Thomas Bayes was (who happens to be buried in Bunhill Fields cemetery in London with William Blake (Songs of Innocence and Experience, Jerusalem), Daniel Defoe (Robinson Crusoe), John Bunyan (Pilgrim's Progress)).

Its chapters are divided into sections forming an Introduction, Foundations, Generalizations, Modeling, Inference, and Remodeling. There is also a section summarizing the basic formulae and alternative non-Bayesian approaches. A rich reference list, subject index, and author index are also provided.

If you are familiar with the math of undergraduate statistics you should not have a problem with the math notation in this book. This really is the standard text you find on most shelves of folks who are familiar with this subject. There are many books to read beyond this one, but this is a fine place to start.

the new bible for Bayesian inference
Recently there have been a wealth of good books published on Bayesian methods and the Markov chain Monte Carlo approach to its implementation. For the beginner Berry's introductory book is a good place to start.

Bernardo and Smith are experts in the field who have participated in many of the Bayesian conferences held in Valencia and much of that lterature is contained in this book. They originally wrote the book in 1993 (with a publication date of January 1994). This paperback edition is not a revision but rather a reprinting with corrections. The original hardcover edition was very expensive and this paperback edition makes the text more affordable and should greatly expand the list of Bayesian specialists and other statisticians and practitioners that read it.

The authors intent was to extend the classical work of Bruno deFinetti who popularized the Bayesian approach with his two classic probability books. One of the authors was involved in translating deFinetti's books into English and they are both well familiar with it. In this book they offer an extension to the area of statistical inference.

The beauty of deFinetti is the logical and systematic nature of the presentation but he did not extend this to statistical practice. These authors maintain the systematic approach and review the probability axioms but then go on to cover statistical modelling including how models are approached through concepts of exchangeability, invariance, sufficency and partial exchangeability. The chapter on inference covers the Bayesian paradigm, the use of conjugate families, asymptotic methods, multiparameter problems and the thorny issues with nuisance parameters. It also includes a number of methods of numerical approximation including Markov chain Monte Carlo (MCMC) methods.

The authors deliberately left the coverage of computational methods brief as they planned a second volume to cover it in detail. But in the preface to the new paperback edition they admit that they have abandon this plan due to the evolution of MCMC methods as the dominant numerical approach and the wealth of new texts that adequately cover the topic.

I suggest that this text is the new bible for Bayesian statistics because I think it replaces the old bibles, Lindley's two volumes (some may argue for Savage's book). This is fitting as both authors attest to being students and disciples of Dennis Lindley. The reason I think it is worthy of bible status is because it covers the foundations in systematic detail, is current and very complete. The text contains references from 1763 (Bayes' original treatise) to 1993 covering an incredible 66 pages of the text. With 20 plus references per page that means over 1320 references!

This is an intermediate level text that requires advanced calculus but not measure theory. Emphasis is on concepts and not mathematical proofs. The authors also provide an overview of the non-Bayesian forms of statistical inference in Appendix B. The authors confront the controversial issues in each chapter. Bayesian statistical methods are treated in the framework of decision theory and ideas from information theory take on a central role.

A must for Bayesians and Non-Bayesians
This book provides a thorough introduction to Bayesian theory and decision analysis. It presents a coherent defense of the subjective view of probability that is driving many new technologies, including probabilistic graphical models, data mining, information retrieval and machine learning, as well as, classical problems such as control and econometrics. It is therefore a must for students and practitioners in these fields. The new, reasonably priced, paper-back version makes the book suitable for university courses on model selection, conjugate analysis or Bayesian statistics in general.

The foundations of Bayesian Statistics made easy
This excellent book presents the foundations of the Bayesian approach to uncertainty in systematic way. Statistical inference is treated as a decision problem which, the authors argue, should be solved on the basis of a subjective probability measure. The emphasis is on ideas rather than technical details and every chapter ends with a detailed discussion of specially important subjects. The list of references is so comprehensive that they alone provide a good reason to buy the book. An absolute must for any true Bayesian, and a perhaps even more necessary book for the yet unconvinced non-Bayesian. ... Read more

Book Description

An up-to-date, comprehensive account of major issues in finite mixture modeling
This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts.
Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-400ublished since 1995
* Includes an appendix listing available mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data. ... Read more

Reviews (3)

superb update on mixture models
McLachlan and Basford (1988) and Titterington, Smith and Makov (1985) were the first well written texts summarizing the diverse lterature and mathematical problems that can be treated through mixture models.Geoff McLachlan is the author of four statistics texts namely (1)McLachlan and Basford (1988) "Mixture Models:Inference and Applications to Clustering", Marcel Dekker, (2) McLachlan (1992) "Discriminant Analysis and Statistical Pattern Recognition", Wiley (3) McLachlan and Krishnan (1997) "The EM Algorithm and Extensions"Wiley and (4) McLachlan and Peel (2000) "Finite Mixture Models" Wiley.These four books are all related to the interesting problems in pattern recognition and clustering.Mixture models and the EM algorithm are tools used to solve problems in clustering and pattern recognition.

In each of his books McLachlan has shown an ability to be clear, authoritative, scholarly and thorough.He provides broad coverage of each topic with detailed references.This book is no exception.As he point out in the preface, the literature on mixture models has expanded tremendously since the appearance of his 1988 monograph with Kaye Basford making an updated text very appropriate.

Almost 40% of the 800 references in the text have appeared since 1995.The recent advances covered in the text include identifiability problems with mixture models, the analysis (fitting of mixture models) for real data sets using the EM algorithm and its extensions, properties of maximum likelihood estimators, applicability of asymptotic theory, use of bootstrap methods to assess accuracy of estimates, implimentation of Bayesian approaches through Markov chain Monte Carlo methods and the use of hierarchical mixtures-of-expert models for nonlinear regression as competitors to the MARS and CART algorithms.

This is a great book.Chapter 1 provides a nice overview of the subject with a thorough historical treatment, nicely presented in Section 1.18.In addition to the fact that it covers all the recent advances one can think of.The book also deals with fast implementations of the EM algorithm for data mining and other approaches to modifying the EM algorithm to handle large data sets.There is also a wealth of interesting real problems worked out in detail.These problems come from many disciplines, including interesting medical problems related to diabetes and hemophilia, nuclear test ban data analysis, image processing and competing risk survival analysis.It also covers some interesting aspects of multivariate normal mixture models and their applications.

Wonderful!
A wonderful text that functions as well as a reference as it does as an introduction to mixture models. I was surprised by the depth and breadth of the book, which manages to describe almost every mixture model imaginable and then some more, including forms of the models themselves, parameter estimation and fit. Relationships between different models are made clear, lending the text a coherence that isn't undercut by vague generalities. The authors are particularly good at addressing issues of particular importance in mixture modeling, such as fit and model selection. Material is suprisingly recent as well. Overall, a great text that is probably destined to become the standard reference on mixture models.

Job well done
Mixture models have become a hot topic in statistics. After you read this book, you will know why.

"Finite Mixture models" have come a long way from classic finite mixture distribution as discused e.g. Titterington et al(1985). A small sample should almost surely entice your taste, withhot items such as hierarchical mixtures-of-experts models, mixtures of GLMs, mixture models for failure-time data, EM algorithms for large data sets, and hidden Markov models.The book gives a lucid overview of recent developments on mixture models since 1990 (the aim of this book in the first place). It expounds on the modern viewpoint that mixtures can be usefully exploited as a mechanism for building flexible statistical models for complex processes, e.g. nonparametric Bayesian models. Balanced attention is given to all three modern approaches to fitting mixture models which include speed-up EM, Bayesian, and stochastic simulation. The whole book is superbly written,and very entertaining---It's hard to put it down once started. It is very update with 45 pages ofreferences and an appendix listing available softwares.

I'm a big fan of Prof. McLachlan's books; and I believe, this latest book of his with one of his student D. Peel, should addanother masterpeiece to the long list of marvelous statistics books coming out of Australia and New Zealand... ... Read more

Book Description

This book integrates technology into the practical introduction of statistics — both Microsoft Excel and MINITAB are incorporated as tools for data analysis. These Excel and MINITAB tutorials give users access to step-by-step instructions and screen shots for using the software to perform the statistical techniques presented in the chapter. Real-world applications and critical thinking skills are emphasized throughout that will allow readers to realize greater success in the workplace.Reorganized content — Rank tests are integrated throughout, dot plots added in Chapter 2, cumulative binomial tables added to appendix, section on the normal approximation to the binomial distribution added to Chapter 6, and goodness-of-fit test of multinomial category probabilities added to Chapter 8.For use as an introduction to statistics reference with a background in college algebra. ... Read more

Book Description

STATISTICS FOR MANAGEMENT AND ECONOMICS, ABBREVIATED SIXTH EDITION is a subset of core chapters from the worldwide best selling and more comprehensive, STATISTICS FOR MANAGEMENT AND ECONOMICS, Sixth Edition (2003). This text teaches students how to apply statistics to real business problems through the authors' unique three-step approach to problem solving. Students learn to IDENTIFY the right technique by focusing on the relationship between the problem and data type. They then learn to COMPUTE the answer either by hand, using Excel, or using MINITAB (SPSS and JMP output and instructions appear on the text Web site). Finally, they INTERPRET the results in the context of the problem. This approach enhances student comprehension as well as practical skills, and offers maximum flexibility to instructors wishing to teach concepts by hand or with the computer, or by using both hand and computer methods. ... Read more

Reviews (2)

Unclear
I used this book a semester ago in my probability class and I must say this is one of the worst textbooks I've come across with.There are plenty of exercises in the book and like the reviewer above said, it covers a lot.However, my friends and I all find the explanations are unclear and unhelpful.

Above average, but that's not saying much for b-stat texts
I have taught MBA Stats using this book for 3 semesters. I have looked carefully at many others, and taught with some of them. Here are some thoughts about Keller/Warrack:

PROS:

All the important topics are covered, and many unimportant ones.

The writing is almost always very clear. The writing is not as good as some economics textbooks I've taught with, but this is true of almost all business statistics textbooks I've seen.

There are plenty of end-of-chapter and end-of-section exercises covering all the key concepts.

Cosmetics - the book has a neat, uncluttered appearance, and the use of color is effective and not distracting. I think this stuff matters, for getting the students to be comfortable spending time with the book, and not making them confused or overwhelmed.

One of the authors, Gerald Keller, is very prompt at returning my emails.

CONS:

Some of the in-text Excel instructions are 90% but not 100% accurate or sufficient.

The "real-world" examples in the probability theory chapters are contrived and unrealistic. This is true of most business stat textbooks, whose authors can't seem to accept that the purpose of these chapters is NOT to illuminate the behavior of the business world, but rather to provide a foundation for a deep understanding of statistical inference (which DOES illuminate the business world extremely well).

The solutions manual has a fair number of mistakes. I'd be more understanding if this was a 1st or 2nd edition, but it's a 6th edition!!! There really is no excuse.

At the end of every semester, I survey my students to get their opinions on various aspects of the textbook. They rate Keller/Warrack acceptable but are not crazy about it. I agree, and have continued using it only because the abundant alternatives are generally mediocre.
... Read more

Book Description

Group sequential methods answer the needs of clinical trial monitoring committees who must assess the data available at an interim analysis. These interim results may provide grounds for terminating the study-effectively reducing costs-or may benefit the general patient population by allowing early dissemination of its findings. Group sequential methods provide a means to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion.Group Sequential Methods with Applications to Clinical Trials describes group sequential stopping rules designed to reduce average study length and control Type I and II error probabilities. The authors present one-sided and two-sided tests, introduce several families of group sequential tests, and explain how to choose the most appropriate test and interim analysis schedule. Their topics include placebo-controlled randomized trials, bio-equivalence testing, crossover and longitudinal studies, and linear and generalized linear models.Research in group sequential analysis has progressed rapidly over the past 20 years. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It provides straightforward descriptions of group sequential hypothesis tests in a form suited for direct application to a wide variety of clinical trials. Medical statisticians engaged in any investigations planned with interim analyses will find this book a useful and important tool. ... Read more

Reviews (1)

clear coverage for practicing biostatisticians
Advances in the theory of repeated significance testing in the 1980s and 1990s has made sequential methods practical by identifying stopping rules for data collected sequentially but in groups. This material is now used to plan interim analyses and both safety and efficacy group sequential trials for clinical trials. This text provides for the first time thorough coverage of these advances with suitable references to the literature.

It should be on the bookshelf of any biostatistician who conducts clinical trials for pharmaceutical or medical device companies. ... Read more

Book Description

This is the most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by two of the most accomplished young econometricians in Europe. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of recently developed nonlinear models, including regime-switching and artificial neural networks, and applies them todescribing and forecasting financial asset returns and volatility. It uses a wide range of financial data, drawn from sources including the markets of Tokyo, London and Frankfurt. ... Read more

Reviews (5)

nice coverage if non-linear time series
Like his other books, Franses provides an nice applied treatment of non-linear time series models that are in this case applicable to finance. It includes extensive coverage of regime switching models. It includes data drawn from several financial markets including Tokyo, London and Frankfurt.

A Long-Awaited Update To Granger and Terasvirta's Book .
The major distinction of the book from Granger&Terasvirta's earlier work is its focus on financial applications of regime switching (RS) models and the author's separate treatment of RS in returns(means) and volatilities(variances) by putting them in different chapters. Another welcome feature is the availability of accompanying procedures in Gauss downloadable from the author's website. I would have expected a lengthier treatment of Markov RS models but I guess either the authors leave this to Tsay's new book or quote Hamilton as classical reference source.

A Long Awaited Update To Granger and Temasvirta's Book
The major distinction of the book from Granger&Terasvirta's earlier work is its focus on financial applications of regime switching (RS) models and the author's strategy of separate treatment of RS of returns(means) and volatilities(variances) by putting them in different chapters. Another wellcome feature is the availability of accompanying procedures in Gauss downloadable from the author's website. I would have expected a lengthier treatment of Markov RS models but I guess either the authors leave this to Tsay's new book or quote Hamilton as classical reference source.

An excellent, up-to-date guide of finance non-linear models
If you are interested in what's up nowadays in the finance modeling, you should have this book. It's a review of some of the more recent, important and promising works of the field. Advanced undergraduate students and graduate students will probably understand the book (although I recommend it mostly for people interested in the field). If you want an easy introduction of most of the topics (but pretty older), then, grab Walter Enders book or, the more complicated, but also more complete book of James D. Hamilton. Reading this manual is easy because it's clear and its style is not boring. If you really love finance econometrics, you'll find this book fun to read. The fields covered by the authors are: 1.-Linear models (pretty brief), unit roots, seasonality and aberrant observations; 2.-Regime-switching models for returns such as TAR (Threshold Autoregressive), SETAR,...; 3.-Regime switching models for volatility (and here you'll have the entire family of ARCH models, with its youngest cousins such as GARCH QGARCH, LSTGARCH, VS-GARCH); 4.-Artificial Neural Network for returns. I'm particularly interested in GARCH-type models, and I can tell this part is particularly well done. At the end of the chapter there is a very illuminating empirical comparison between the models. I cannot say if the "artificial neural networks" is a good chapter since I'm not an expert, but the least I can say is that it's pretty understandable (although quite challenging for an ignorant like myself).

A timely survey on an important area
The title of this book caught my attention immediately and it actually contains more interesting topics than I thought. After I bought a copy through Amazon and have a closer read, I'm not disapointed by the two authors' writing, which is probably partially based on the second author's PhD dissertation, and so it is a little narrow-focused. But as the authors stated, they want to produce a book which deals with nonlinear techniques as opposed to Mills's mostly linear methods in fiance time series. They have delivered. With hot topics such as regime switching, ARCH models, and neural network applications in finance, I'm sure this book will find a lot of interested readers and will be a key reference in nonlinear empirical finance. ... Read more

Book Description

This eighth edition of McCall's well-respected book continues to present concepts in a way that students can easily understand. The new edition has been updated throughout and now includes recommendations by the APA Task Force on Statistical Inference. As in previous editions, McCall helps students see the many real applications of statistics to research in the behavioral sciences. Taking a traditional approach to teaching the basic statistical concepts and methods used in behavioral research. McCall emphasizes building an understanding of the logic of statistics rather than stressing the mechanics. In this exciting revision, McCall continues to keep the data for the computational problems simple, so your students can focus on the rationale and outcome of techniques rather on the calculations themselves. Using clear discussion, a wide variety of end-of-chapter exercises, and examples drawn from actual studies, McCall helps students learn how to choose appropriate statistical methods and correctly interpret the results. Also retained in this edition are the author's step-by-step explanations for each proof and his clear definitions of symbols--the essential vocabulary of statistics--that have been so successful in helping students master the material. ... Read more

Reviews (1)

The Joy of Statistics?
Although it is a rare segment of the human population who could claim to find scientific statistics "enjoyable", Robert McCall has somehow managed to take some of the pain out of what is generally deemed a horrifying experience. I thank Mr. McCall and the hands-on approach of his book for enabling me to make it through Stats this time with a passing grade! ... Read more

Book Description

* Contains additional discussion and examples on left truncation as well as material on more general censoring and truncation patterns.
* Introduces the martingale and counting process formulation swil lbe in a new chapter.
* Develops multivariate failure time data in a separate chapter and extends the material on Markov and semi Markov formulations.
* Presents new examples and applications of data analysis. ... Read more

Book Description

This is an introductory textbook on probability and induction written by one of the world's foremost philosophers of science.The book has been designed to offer maximal accessibility to the widest range of students (not only those majoring in philosophy) and assumes no formal training in elementary symbolic logic.It offers a comprehensive course covering all basic definitions of induction and probability, and considers such topics as decision theory, Bayesianism, frequency ideas, and the philosophical problem of induction.The key features of the book are:* A lively and vigorous prose style* Lucid and systematic organization and presentation of the ideas* Many practical applications* A rich supply of exercises drawing on examples from such fields as psychology, ecology, economics, bioethics, engineering, and political science* Numerous brief historical accounts of how fundamental ideas of probability and induction developed.* A full bibliography of further readingAlthough designed primarily for courses in philosophy, the book could certainly be read and enjoyed by those in the social sciences (particularly psychology, economics, political science and sociology) or medical sciences such as epidemiology seeking a reader-friendly account of the basic ideas of probability and induction.Ian Hacking is University Professor, University of Toronto. He is Fellow of the Royal Society of Canada, Fellow of the British Academy, and Fellow of the American Academy of Arts and Sciences. he is author of many books including five previous books with Cambridge (The Logic of Statistical Inference, Why Does Language Matter to Philosophy?, The Emergence of Probability, Representing and Intervening, and The Taming of Chance). ... Read more

Reviews (3)

Hacking gets everything right except for Keynes
Hacking's book is a job well done.He blends history,philosophy,logic,mathematics,statistics and science with wit and judicious scrutiny in general.Unfortunately,the book is slightly marred by inaccurate and/or incorrect statements about J. M. Keynes and/or his logical theory of probability.Describing Keynes as a"belief dogmatist"is way off the mark given Keynes's penchant for changing his mind as new and/or relevant information and analysis became available over his lifetime.Secondly,it is bizarre for Hacking to claim that Keynes had no use for frequency-type probability theories and jeered at the idea of relative frequency holding in the long run because in the long run we are all dead.(Hacking,pp.146-151).The only frequency theory Keynes ever rejected was that of John Venn.Keynes always considered frequency theories to be accurate and correct for some cases.However,they were not general in scope but limited in their applicability.The interested reader should consult chapter 8 of Keynes's A Treatise on Probability(1921).Finally, Keynes rejected the fallacy of long runism or conditional apriorism because of its unsound argument.The fact that in the long run some process may converge to a particular outcome in the limit offers no support to a do-nothing policy in the present.If the only available relevant evidence bearing on the probability of a proposition is frequency data then the logical probability is the same as the relative frequency estimate.The only caveat Keynes would add would be that the frequency data should have passed the Lexis Q Test for stability.

For anyone, any thinker
I would HIGHLY recommend this book for anyone (including business men) who must make decisions with incomplete information and under uncertainty. Instead of focusing on the mechanics of statistics, it focuses on how to think about risky propositions.

I bought this book while working on a particular problem in machine learning, at a point where I had started realizing that I was losing clarity on my definition of probability. I was using the mechanics, but didn't clearly understand why the use was valid. This seemed an odd and embarrassing circumstance at the time, how could I not understand what "probability" means? As it turns out this confusion is one shared broadly in history of science, and in current applications of statistical mechanics.

Prof Hacking's writing is clear and entertaining, clearly aimed at engaging the reading audience.

What do you mean, "probably"?
The best thing about this book is that it teachs basic probability theory while keeping the reader constantly aware of the on-going debate regarding what it means to talk in terms of probabilities, and of how that debate has shaped the development of probability theory. If you are a student taking a course in probability and statistics who would like to genuinely understand the conceptual basis of all those formulas they are teaching you, I suggest you read this book.

Some readers will be disappointed by this book. Since the book concentrates on the conceptual basis of probability and inductive logic, it does not give the reader enough technical tools to really do much applied mathematics. On the other hand, by the time Hacking gets around to discussing what students of philosophy will likely view as the big philosophical pay-off of probability theory (i.e. Bayesian and frequentist contributions to the problem of justifying induction) he devotes to them a mere 20 pages of not terribly deep discussion. ... Read more

Book Description

Since the publication of the first edition of this classic textbook over thirty years ago, tens of thousands of students have used A Course in Probability Theory. New in this edition is an introduction to measure theory that expands the market, as this treatment is more consistent with current courses.

While there are several books on probability, Chung's book is considered a classic, original work in probability theory due to its elite level of sophistication. ... Read more

Reviews (2)

excellent text on probability
This text by Chung was one of the texts that I used when I was taking a graduate course in probability at Stanford in 1975. It is carefully written but challenging. It provides good coverage of the central limit theorem, the law of large numbers and the law of the iterated logarithm. It also covers stable laws very well. The style is one of rigorous mathematics with theorems, and lemmas given with their mathematical proofs.

The book was recently revised. The revised text does not change much but new material on measure and integration that is now commonly included in the first graduate course in probability has been added. In the 1970s at Stanford a course in measure theory was a prerequisite for the course in advanced probability although some student took it concurrently.

If you plan to get this text, the revised edition is probably worth it. If you already have this edition and know your measure theory, it may not be worth it to get the new edition.

the best buy
"A course in probability theory", written by Kai Lai Chung, has been referred by not only mathematicians but also mathematical economists.This book is written very rigorously, but almost all of the theorems have easy-to-understand proofs. So it is not difficult to follow. Moreover, there are lots of exercises in this book. So I do recommend this book. ... Read more

Book Description

Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp. ... Read more

Reviews (2)

Bollen still has what it takes
Bollen is comprehensive and thorough, sometimes even to a fault. This work is definitely for the advanced academic who works with structural equations on a regular basis - certainly not for the faint of heart.

Although the field has made advances in the thirteen or so years since this book was first published, I found that Bollen has weathered well, is still suitable for instruction and practice, and should remain so for many years to come.

A bit stiff, and a bit dated, but a classic text
This is the definitive textbook on structural equation models with latent varibles. It is a bit dated now, and the treatment is rigorous, but any serious user of LISREL, EQS, etc. should purchase and read this book. For what it's worth, Bollen is not only a good writer, but a great teacher. ... Read more

Book Description

In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques. An Introduction to Credit Risk Modeling supplies both the bricks and the mortar of risk management. In a gentle and concise lecture-note style, it introduces the fundamentals of credit risk management, provides a broad treatment of the related modeling theory and methods, and explores their application to credit portfolio securitization, credit risk in a trading portfolio, and credit derivatives risk. The presentation is thorough but refreshingly accessible, foregoing unnecessary technical details yet remaining mathematically precise.Whether you are a risk manager looking for a more quantitative approach to credit risk or you are planning a move from the academic arena to a career in professional credit risk management, An Introduction to Credit Risk Modeling is the book you've been looking for. It will bring you quickly up to speed with information needed to resolve the questions and quandaries encountered in practice.Features"Concisely presents the most fundamental and up-to-date concepts of credit portfolio management "Introduces modeling frameworkssuch as KMV, CreditMetrics, and CreditRisk+"Presents best practices in credit risk modeling "Keeps mathematical proofs to a minimum while remaining mathematically solid ... Read more

Reviews (2)

Excellent Book For Credit Risk Managers
This is an excellent book covering the latest thinking in credit risk modeling. It could have used more on documentation and structuring risk, so I only gave it four stars, but it is five stars in model review.

For more on product descriptions and structuring risk, I highly recommend Tavakoli's "Credit Derivatives" 2nd Edition.

CreditTrader
This is an excellent treatise on the near state-of-the-art in credit risk management. Although the focus is on sell-side risk management, many (if not all) of the techniques described can be used on the buy-side also.

This is the first book that really focusses on the portfolio problem of credit risk - many books have touched on vendor-provided models and their shortcomings but Bluhm et al. take it further into the practitioner's world.

The reader does not need a very strong background in math or physics but some understanding of finance and stochastic calculus would help to get the most out of it.

I recommend to everyone who is either in or thinking of getting into credit risk as a career - enjoy.... ... Read more

Book Description

Understandable Statistics is a thorough, yet manageable text for one-semester introductory statistics courses. The text's approachable style is designed to help students overcome their apprehension about statistics. To reinforce this approach, the book integrates graphing technology as well as real-life data from such sources as journals, periodicals, newspapers and the Internet. This real-world emphasis asks students to utilize real data to draw conclusions and interpret results, working on problems drawn from the sciences, business, medicine, archaeology, and consumer economics.

• Check Points at the beginning of each section list the concepts that will be covered in the section to prepare students for the material.
• Tech Notes, found in select section examples, provide general tips to guide students in the appropriate use of the TI-83 Plus, Minitab, and Excel. Denoted by an icon, the Tech Notes also incorporate display screens from each of these technologies to help students work through a statistical problem and better understand the solution.
• Chapter openers have been redesigned to include new pedagogical features: a brief Chapter Table of Contents and a set of Preview Questions with references to the appropriate section in the chapter. Also featuring compelling new photos, the chapter-opening material maintains the book's emphasis on real-world applications with quotes, commentary, and Focus Problems from the previous edition.
• Even and odd answers in text margins of the Instructor's Annotated Edition, located next to their respective exercises, offers instructors a time-saving convenience. For answers that contain art or tables too large to fit in the margin, a note directs instructors to the Appendices.
• Viewpoint boxes present a real-world situation and feature thought-provoking problems that help to humanize statistics.
• Data Highlights (Group Projects) at the end of each chapter help prepare students for collaboration in the workplace.
• Linking Concepts (Writing Projects) at the end of each chapter challenge students to extend their thinking and examine statistical concepts from a broader perspective by expressing their thoughts in essay form.
• Guided Exercises immediately follow selected examples to give students the opportunity to work with new concepts before more are presented. Completely worked out solutions are situated beside each exercise to offer immediate reinforcement.
• Detailed Examples show students how to select and apply appropriate procedures to solve problems.
• Chapter Focus Problems help prepare students for the concepts and skills to be covered and comprehensive Chapter Review Problems at the end of each chapter reinforce the material. Many problems synthesize material and concepts from several sections, asking students to decide what technique to apply to a problem.

Book Description

This completely revised text provides a simple but rigorous introduction to probability. It discusses a wide range of random processes in some depth with many examples, and gives the beginner some flavor of more advanced work, by suitable choice of material. The book begins with basic material commonly covered in first-year undergraduate mathematics and statistics courses, and finishes with topics found in graduate courses. Important features of this edition include new and expanded sections in the early chapters, providing more illustrative examples and introducing more ideas early on; two new chapters providing more comprehensive treatment of the simpler properties of martingales and diffusion processes; and more exercises at the ends of almost all sections, with many new problems at the ends of chapters. The companion volume Probability and Random Processes: Problems and Solutions includes complete worked solutions to all exercises and problems of this edition. This proven text will be useful for mathematics and natural science undergraduates at all levels, and as a reference book for graduates and all those interested in the applications of probability theory. ... Read more

Reviews (8)

Beauty magazine makes you look ugly, if you get my drift.
Beauty magazine makes you look ugly, if you get my drift.

If a textbook has anything to deliver, it should explain, show and demonstrate to the reader the ideology, argument and conclusion of the topic at hand. I am unfortunate enough to own this book and it failed in all counts. In fact many of my college mate (at MSc level) get more confused AFTER reading this book. I would say pay less and get the Outline book series instead.

Recommended
This book provides a very broad coverage of probability and stochastic processes. The problem sets require a decent amount of effort for none of them are trivial. One of the better books for probability.

Too advanced for beginner
I think this book is good but very difficult to read. If you are beginners of probability, I suggest read other books like Sheldon Ross's "First Course in Probability" or "Introduction to Probability Models".

A Very Terse Treatment of Graduate Level Probability
I was a bit surprised that