Book Description

This book provides a self-contained account of a wide range of statistical methods for the analysis of longitudinal data. Emphasizing the biomedical and agricultural sciences, the book covers each method's applicability and underlying statistical theory.Major topics include: design considerations, exploratory methods of analysis, linear models for continuous data, generalized linear models for discrete data, and models and methods for handling data with missing values.Worked examples are presented throughout and an appendix covers some basic statistical principles.This cogent and clear text will be welcomed by students across a wide range of the sciences. ... Read more

Reviews (3)

the long awaited second edition
The second edition is much like the first and is at least a year behind the original schedule. See my review of the first edition to understand why this is a classical. The promised advances in missing data are included and a new author Haegerty has been added. The missing data chapter is three times longer than in the first edition. They cover what they promised. They also mention some of the econometrics literature including the work of Nobel Laureate James Heckman but admit in the preface that they do not know that literature very well and hence do not cover it in depth.

In the past two years Verbeke and Molenberghs have produced a highly competitive book that deals in detail with pattern mixture models and other missing data methodology but curiously Diggle et al. do not reference it even though they do cite some of Molenberghs work.

already the classic book on longitudinal data analysis
When this book came out in 1994 there was a great need to look differently at clinical data on subjects. Typically such data would have repeated measurements over time for many subjects but for only a few time points (say three to five). Standard analysis of variance methods do not properly account for within patient correlation between measurements. Time series analysis generally is good for treating long series (but usually only one or a few). In the clinical setting we often are considering hundreds of patients over short time intervals. This book is clearly written for intermediate level statistics students.

The field is important and rapidly developing. Though slightly dated the book is still an excellent introduction to the subject and a very good reference. However, a second edition is in the works and should be out in about one year. I recently took a short course from the authors and I know that the second edition will have some nice features including the latest advances for dealing with missing data and ways to combined the information from time to event data with the repeated measures data. It may be that if longitudinal data analysis is important to you, read the first edition at your favorite university library and save your money for the second edition.

The book includes some nice treatment of the important but often neglected topic of sample size determination.

Excellent, highly recommended!
This book was written by three very prestigious authors, two of which work at The Johns Hopkins University(Dr. Liang and Dr. Zeger), and Dr. Diggle, who is working in England. These three are very well known and respected characters in their field of work, and this book is an excellent reflection upon the research and work they have done over the years. Watch out! the key word is: GEE ... Read more

Book Description

PROBABILITY AND MEASURE

Third Edition

Now in its new third edition, Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Retaining the unique approach of the previous editions, this text interweaves material on probability and measure, so that probability problems generate an interest in measure theory and measure theory is then developed and applied to probability. Probability and Measure provides thorough coverage of probability, measure, integration, random variables and expected values, convergence of distributions, derivatives and conditional probability, and stochastic processes. The Third Edition features an improved treatment of Brownian motion and the replacement of queuing theory with ergodic theory.

Like the previous editions, this new edition will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory. ... Read more

Reviews (7)

Some nice examples, poorly organized
This is probably a very nice text book if you already know probability. There are undeniably some insightful examples. However, it is often hard to follow the sequence of topics in the book.

It is at least amusing that the integral is only developed a couple of chapters after expectation has been in use...

a very good text book
This book gives abundant examples and statements that help to deepen your understanding. It does not require much statistic background to follow the book, although sometimes the reasoning is not that obvious to me. But maybe because I am not a math student. I also feel that topics are a bit scattered in the book.

Standard text but ...
the main problem with Billingsley's book lies in its organization of topics and results.
Yes it has all the standard results that need to be covered in a first (rigorous) course on probability theory and the proofs and exercises are good (thats why the three stars) but it is incredibly hard to study them from this book because of poor organisation which makes for lack of continuity (thats why no more than three).
Stick to Chung (and move to something more specialized thereafter). Unfortunately, Parthasarathy's 1977 Macmillan book is now out of print and only available in libraries ... I find that to be the best book at this level.

Great text
I have found Billingsley's text to be the most understandable probability/measure theory text that I have encountered. It is not necessary, but a basic background in measure theory would be very helpful.

Probability Theory Bible
"Probability and Measure" by P. Billingsley covers a lot of topics in probability theory, and in this sense it is a standard reference, but what I did not like much is that the concepts are somewhat scattered around the book, so one has to jump back and forth all the time. (May be this is an artefact of my graduate course in probability theory that had topics in the syllabus ordered in a way different from Billingsley.) I found another book more useful and more clearly written -- see Borovkov et. al., "Probability theory". ... Read more

Book Description

"There is no book on the market to compare with Dr Jäckel's. All the techniques, the tricks, the pitfalls of this important methodology are covered in detail and with great insight. This is no book on abstract theory, Dr Jäckel is a practitioner who has implemented every single one of these ideas. He has done all the hard work, so you don't have to." Paul Wilmott

"Few expert practitioners also have the academic expertise to match Peter Jäckel's in this area, let alone take the trouble to write a most accessible, comprehensive and yet self contained text. This book is a delight to read and contains a wealth of information that is essential for anyone involved with implementing Monte Carlo methods in finance." Professor Carol Alexander, ISMA Centre, University of Reading, UK

" This book is a very welcome addition to the growing literature on applied quantitative methods in finance. Dr Jäckel has done the field a service in combining both a thorough review of 'standard' material with techniques that were learned on the job as a quant at top financial institutions. Michael Curran, Quantin' Leap

Based on the author's own experience, Monte Carlo Methods in Finance adopts a practical flavour throughout, the emphasis being on financial modelling and derivatives pricing. Numerous real world examples help the reader foster an intuitive grasp of the mathematical and numerical techniques needed to solve particular financial problems. At the same time, the book tries to give a detailed explanation of the theoretical foundations of the various methods and algorithms presented.

Monte Carlo methods have been used in the financial community for many years for addressing complex financial calculations. Recent advances by both practitioners and academic researchers in the area of fast convergence methods, together with the improvements achieved by the manufacturers of computer hardware, make Monte Carlo simulations more and more frequently the method of choice. In this long needed book on modern Monte Carlo methods in finance, Peter Jäckel provides an introduction to many of the leading edge techniques available. ... Read more

Reviews (7)

for Quants only
if you're a quant, you might really love this book

if you're a person who wants to have a "basic" understanding how to use MC for consulting or product pricing with examples, you got the wrong book (not mentioning that your maths must be pretty good).

if you're looking for an Excel example on how to price some basic options, i highly recommend Jackson & Staunton or Wilmott.

Good book
This book is pretty good as it covers lots of different areas of Monte Carlo simulation and some of the newer stuffs, such as copulae, etc. The math presentation is brief but to the point as application of the mathematics to Monte Carlo methods is the emphasis. Intuitive ideas behind the formula is explained pretty well as it tells you where certain formula can be used for. It would be helpful to have taken an advanced course in Monte Carlo methods in Finance to appreciate the book. I would personally suggest Glasserman's course at Columbia U. Prof Glasserman is also writing a book on the subject that he uses for lecture notes now. It would turn out to be an even better book to read.

Competent Treatment of an Advanced Approach
This is an excellent resource for anyone already familiar with Monte Carlo modelling. Scientists making the transition to Wall Street will find this a needed supplement to Hull and other good resources. Product descriptions are also needed, especially for areas in which growth is exploding and therefore jobs are available. "Credit Derivatives" (Second Edition) by Janet Tavakoli is a great resource for getting up to speed on these products and for highlighting some of the data and modelling issues one will encounter. Although it is a product book and an applications book that helps the modeller understand how to approach the problem.

An advanced approach to math methods behind finance
Very interesting and well written book reviewing more advanced mathematical concepts which might be relevant for finance engineering - not limited to Monte Carlo methods. The author seems to have a firm background in theoretical physics. Definitely not for simpletons.

CD does not work
It is a book for mathematics lovers not financial oriented profesionals. I would not recomend this book for those looking to gain more practical knowledge on this subject. ... Read more

Book Description

Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis.To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containingfull-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.).Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field. ... Read more

Reviews (4)

Far from superficial...
When entering the minefields of microarray data analysis, one has to understand and keep up with state-of-the-art technologies and interdisciplinary literatures. A background in molecular biology is clearly not enough to evaluate the pro and cons of the various statistical methods for selecting truly modulated candidate genes in a given experimental biological system. Choosing between the available analysis software's is not an easy task either. Draghici presents a complete visit of the microarray underworld by initiating the reader to all the facettes of this domain. From the fundamentals of slide production and target hybridization to image processing, statistical analysis, experimental design, data management and biological interpretation, all aspects treated herein are described with pertinent details. Draghici slowly, but successfully, tames the reticent molecular biologist to the arid world of statistics and even entertains the reader with anecdotes and humoristic citations.
Clearly written, with appropriate mathematical examples for each topic, this book even includes exercises at the end of some chapters, for the zealous student sleeping in all of us. It constitutes a very good didactic tool and the included CD's allow a good peek in some of the available image/data analysis software's on the market.
As a core facility manager and eternal student, I strongly recommend Draghici's book to life scientists and students who are struggling with statistical analysis and data mining techniques.

Brigitte Malette, Ph. D.
Project Leader, Microarray Platform
Centre for Structural and Functional Genomics
Concordia University
Montreal

Detailed and understandable
Draghici managed to write a manual on applying microarray (data) with a great feeling for explanation of hard issues. The book is relatively easy to read, very complete and covers most, if not all, analysis techniques that are currently around for microarrays.

Highly recommendable!

Good Overview of Microarray Technology
I have had the book for about a month now and I consult it quite frequently. Great coverage of Microarray Data Anlysis. It manages to be thourough without being dry or using excessive jargon. It's very readable and useful for both novices and experienced readers.

It's main strength lies in the use of excellent examples that show the main pitfalls encountered in analyzing microarray data. It has great coverage of statistics and their potential misuse and misunderstanding when they are applied to gene expression data sets. The experimental design section is especially helpful for researchers that are designing a project.

The graphics are excellent and the book is printed on good quality paper.

The book includes two CD's with demo versions of several commercial software packages.

Overall a great buy.

Data Analysis Tools for DNA Microarrays
A much needed book for the biologist interested in using DNA/protein microarrays. Examples are specific for microarrays. The material starts from ground zero and begins
with image analysis. All major methods for analysis are discussed.
Well worth the cost, quality graphics, includes software (have not used as yet).
A must read before discussing experimetnal design with your stats person. ... Read more

Book Description

Knowledge spaces offer a rigorous mathematical foundation for various practical systems of knowledge assessment. An example is offered by the ALEKS system (Assessment and LEarning in Knowledge Spaces), a software for the assessment of mathematical knowledge. From a mathematical standpoint, knowledge spaces generalize partially ordered sets. They are investigated both from a combinatorial and a stochastic viewpoint. The results are applied to real and simulated data. The book gives a systematic presentation of research and extends the results to new situations. It is of interest to mathematically oriented readers in education, computer science and combinatorics at research and graduate levels. The text contains numerous examples and exercises and an extensive bibliography. ... Read more

Book Description

"I think that the coverage of the text is excellent. It carves out a seriously neglected area and it very thoroughly covers the topic. The authors are very knowledgeable concerning the literature. This is an excellent text that provides a detailed, yet comprehensible account of how to estimate, test, and probe interactions in regression models."

--David A. Kenny, University of Connecticut

"Leona S. Aiken and Stephen G. West do an excellent job of structuring, testing, and interpreting multiple regression models containing interactions, curvilinear effects, or a combination of both. Procedures for testing and graphical displays of interactions between categorical variables have been done for years but none seems to have provided a comprehensive treatment or guideline for the analysis of interactions between continuous variables. . . . Aiken and West, however, address those issues quite effectively and thoroughly. . . . An aid to any graduate and/or researcher in their analysis of continuous variables. Highly recommended for graduate libraries."

--Choice

"The book would serve very well as a reference for applied researchers and methodologists. . . . In particular, this would be an excellent reference for anyone who encounters a multivariable prediction problem and has reason to believe that either a nonlinear model or a model including a variable product term would be appropriate."

--Contemporary Psychology

Researchers in a variety of disciplines frequently encounter problems in which interactions are predicted between two or more continuous variables. However, the current literature regarding how to analyze, interpret, and present interactions in multiple regression has been confusing. In this comprehensive volume, Leona S. Aiken and Stephen G. West provide academicians and researchers with a clear set of prescriptions for estimating, testing, and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models or for those enrolled in courses on multivariate statistics.

Reviews (7)

revolutionary
This book has revolutionized the way psychologists think about interactions. It provides step-by-step instructions on how to probe the moderating effects after you find a significant interaction in a multiple regression.

The basic idea about interaction is that the relationship between two variables were different according to a third variable. For example, some risk factors (such as poor family income) may affect children's academic achievement in a negative way. However, if the parents provide enough support on their children's study, then it's possible that the risk factors will no longer influence their children test scores. Therefore, with low support, risk factors are very effective, but with high support, risk factors have not effects. This book teaches you how to probe this relationship in a systematic way, it covers 2-way, 3-way interactions and also quardratic relationships.

If you fully understand this book, the techniques you have will be enough for a masters thesis in your area.

A MUST have for anyone using regression analysis
This book takes a very practical approach to the analysis of interactions in regression. No other book I've used has covered these topics as clearly or in as much depth. The extensive discussion of decomposing interactions is a prime example. With the push to replace old techniques of dichotomizing continuous variables with a continuous (regression) treatment of these variables (especially in psychology), this book is extremely important.

Invaluable and accessible
Back when the book was first published, I was completing doctoral research. Aiken & West provided the explanations and instructions that enabled me to complete my dissertation. Nowhere else have I seen the information they provide; seldom have I seen statistical treatments as clearly and easily explained. Like many in the social sciences, math was not my greatest intellectual ability. This book made computing and understanding regression interactions a relative breeze. One reviewer bemoaned the lack of information on interactions among categorical variables. I suppose he didn't read the preface that specifically explains the reason for the absence: such information is widely available in any good statistical text. What Aiken & West provide can't be found elsewhere in any real depth. I am ordering another copy of the book because I'm tired of loaning out my copy to colleagues, especially one who has now begun to copy whole chapters. Yes, it's that useful.

Lacking
Yes, there is some good information and discussion in this book but for the price I would expect it to be more complete. For example, there is absoultely no mention of interactions between two categorical variables. I guess the authors ran out of steam. Also, the writing could have used some more refinement. I'd stick with Jaccard's volumes in the Sage Quantitative Applications series.

THE reference for interactions in multiple regression
While the presentation and writing might not be quite perfect for some readers, this book provides the best coverage of handling interactions in multiple regression that I've yet seen. Everyone who does multiple regression with any regularity needs to have this book on their shelf. ... Read more

Book Description

Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies. ... Read more

Reviews (8)

Get a more recent title for the important modern advances
A solid excellent DOE book, however due to it's age, it obviously does not cover more recent topics, such as mixture experiments. I've run into a few chemical engineers that have read only this book and have no idea what mixture experiments are, and why they are important in their DOE work. Also, I do not remember seeing any material on split-plot designs, and this topic is very important in industrial experimentation since most experiments are split-plots whether you know it or not, and you cannot evaluate them as normal. This is no fault of the book due to its publish date, but a newer book, such as Montgomery's or Hamada & Wu should also be read through to learn about the more recent advancements in DOE.

very useful
A great book to understand the theory and the application of statistics. The examples used in each chapter are very useful in understanding the concept.
I suggest this book to every researcher and instructer to keep it on their desks. "This" is the reference.

Immediate usability in practice.
This is an excellently written book with clear examples of how to apply statistics to everyday experimental settings. Box delves deep enough into the underlying theory to give an engineer such as myself an appreciation for the "reality" of the mathematics, but sticks to concrete examples and putting theory into practice. Each chapter follows the previous one, but each is also reasonably self-contained. Terminology is easily clarified with a quick use of the comprehensive index.

Additionally, don't let the print date fool you... the book is timely.

classic but unconventional and practical book on design
This book was published in 1978 but as other reviewers have noted its practical methods and advice are timeless. George Box and Stu Hunter are both very famous statisticians who are also great teachers and lecturers. Bill Hunter is now deceased. All three authors have made major contributions to the design of experiments. The book is written for practitioners and in the simplest language possible. Emphasis is placed on practical designs and not optimal designs because optimal designs are very sensitive to model specification.

It does not include the robust designs of Taguchi which came later and could easily be included if the authors choose to revise it.

Still the "Bible" of practical design of experiments.
More than twenty years after its publication, this seminal work is still the undisputable "Bible" for users of statistical experimental design. The practical insights sprinkled throughout this book are invaluable especially to non-mathematical statisticians. This book will never be out-of-date! ... Read more

Book Description

This well-respected text gives an introduction to the modern approximation techniques and explains how, why, and when the techniques can be expected to work. The authors focus on building students' intuition to help them understand why the techniques presented work in general, and why, in some situations, they fail. With a wealth of examples and exercises, the text demonstrates the relevance of numerical analysis to a variety of disciplines and provides ample practice for students. The applications chosen demonstrate concisely how numerical methods can be, and often must be, applied in real-life situations. In this edition, the presentation has been fine-tuned to make the book even more useful to the instructor and more interesting to the reader. Overall, students gain a theoretical understanding of, and a firm basis for future study of, numerical analysis and scientific computing. A more applied text with a different menu of topics is the authors' highly regarded NUMERICAL METHODS, Third Edition. ... Read more

Reviews (22)

A good textbook and handbook for real analyzer
In my first year of graduate life, I implemented many algorithms
on this book, and made them workfairly well.

The pseudo code in the book is very clear while the proof of theorem and algorithm are easily to be understood.

To learn from the book is an enjoyment to me.
Frankly speaking,it took me into the field of Numerical Analysis.

Maybe you might thought the proof and convergence analysis
were a little dull,but please notice they are esscential to
a professional analyzer.

full of errors
I normally don't write reviews for books, but I felt compelled to say that this book has quite a few errors that I've personally found quite annoying. The errors aren't mentioned in the authors' online errata either, which covers only the 1st printing. I'd think you could iron out most bugs after 7 editions, but apparently not. The coverage of material itself, while not great, is acceptable, but there are random errors scattered throughout that threw me off. At least a few of the algorithms, when implemented, don't work properly. Some of the solutions in the back aren't accurate or are just wrong (e.g., some ask for what h you need to be below a certain error bound, then proceed to give a larger h than is really necessary). Just my two cents.

Numerical Analysis for Dummies its not...
This book covers all the topics a reader would expect of numerical analysis and comes with a CD of pre-built code for many of the analysis techniques. From my perspective, the authors' present theorem and proof with relatively few examples. I found myself referring to Gerald and Wheatley's Applied Numerical Analysis (among others) for the duration of my college course to attain the level of understanding expected by the university. Gotta love libraries! At $.., this is the most expensive math book I've purchased, and I can say that I wouldn't value it at this price if it had not been selected by the university. Best of luck to those who read it...

Review of Numerical Analysis, 7th edition
This is a numerical analysis book written from a mathematician's point of view, and requires from the reader a good background in calculus and linear algebra.

Even though the book has an initial chapter ("mathematical preliminaries"), reading this chapter is not enough if the student has not a good previous mathematical knowledge.

The book introduces modern approximation techniques and explains how, why and when these techniques are expected to work, and allows the reader to understand why one algorithm works better than other for a given problem.

The text contains many examples as well as application problems in various areas of science and engineering.

The book uses Maple as the standard software for symbolic and approximate calculus, even though Mathematica and Derive are mentioned too and could be used instead with small modifications.

The original English edition (7th edition) includes a CD-ROM with all the algorithms, expressed in different formats (C, Fortran, Pascal, Maple, Mathematica and MATLAB), although the Spanish translation (edited by Thomson Learning) does not include the CD-ROM. However, there is an Internet address in which the CD-ROM contents can be accessed.

To conclude, the book is a good text that requires a mathematical background from the reader and covers a broad range of modern approximation techniques. It is not a mere numerical methods cookbook, but a text that analyzes and applies the numerical methods instead.

Wordy, poor algorithms, worse code
Like other reviewers, I'm still struggling to find a decent advanced mathematics textbook.Some of the problems with Burden's book includes insufficient examples and explanations.He introduces strange and unnecessary notation in his algorithms; for example in chapter 7 (Iterative techniques for solving linear systems) many of his index loops run from 1 to n.If he set them from 0 to n-1, it would clean up much of his logic.He also apparently loves the variable XO to represent the initial approximation x naught.

Maybe due to my physics background, but his notation of representing indexes of variables as a _power_ is confusing:
Burden represents the i-th index of x as x^(i), not to be confused the i-th power of x: x^i.Modern typesetting includes subscripts, why not use them instead?Heck, use LaTeX and do the same thing (x_i)!

Finally, several of the codes on the included CD refused to run, and some of them didn't give correct answers.You will need some programming experience to edit, as none of the codes (at least all of the Matlab and possibly all of the C) adhere to any programming standards or formatting.Mr. Burden (or his programmer) is invited to purchase and use Steve McConnell's "Code Complete"--or hire someone who knows how to write maintainable code well.What is the purpose of supplying code if it cannot be used in other projects?"Gee Wiz, the book includes Code!" one might exclaim. "But what good is it?" is the inevitable response. ... Read more

Book Description

Sheldon Ross' Simulation, Third Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes.

This new edition provides a comprehensive, in-depth, and current guide for constructing probability models and simulations for a variety of purposes. It features new information, including the presentation of the Insurance Risk Model, generating a Random Vector, and evaluating an Exotic Option. Also new is coverage of the changing nature of statistical methods due to the advancements in computing technology. ... Read more

Reviews (1)

A reader from Saudi Arabia
This is an excelent textbook explaining what simulation means and how to deepen your knowalge. You have to be a good programmer in order to use this book (and simulation generally)and the author should have added an andex for such a language and how its connection with simualtion ( C or C++) although my experience would elect MATLAB as prefernce!! This text does not requir any prior experience regarding simulatin although taking a course in statistics and probability would be advantageous!! ... Read more

Book Description

From the reviews: "This is a magnificent book! Its purpose is to describe in considerable detail a variety of techniques used by probabilists in the investigation of problems concerning Brownian motion. The great strength of Revuz and Yor is the enormous variety of calculations carried out both in the main text and also (by implication) in the exercises. . . . This is THE book for a capable graduate student starting out on research in probability: the effect of working through it is as if the authors are sitting beside one, enthusiastically explaining the theory, presenting further developments as exercises, and throwing out challenging remarks about areas awaiting further research. . . " Bull. L. M. S. 24, 4 (1992) Since the first edition in 1990, an impressive variety of advances has been made in relation to the material found in this book. ... Read more

Reviews (2)

a comprehensive book on stochastic calculus, yet accessible
I only read about 70% of the text, without essentially touching
the excercise problems. I have to confess I'm pretty much overwhelmed by the myriad topics treated in this book.

From the perspective of a student, I think Revuz/Yor has the following merits:

1. It covers an enormous amount of materials, systematically and
carefully. It thus provides the necessary preparation for a graduate student who's eager to get ready for research.

2. Despite of its scope, this book is accessible to graduate students. By "accessible", I mean any dilligent student with certain mathematical maturity should be able to understand most of the materials in the text.
Two things about this book make possible the accessibility. First, proofs are very carefully written, and a quite few of them may even be called detailed. Second, the authors deliberately chose the "slickest" approaches to many classical results,
while preserving, even elucidating, the fundamental ideas. Examples include the construction of BM from the perspectife of Gaussian processes, the presentation of Markov processes in Chapter 3, the "global" definition of a stochastic integral, etc.
This paves the way for further study of more general cases.

3. The computations displayed in this book can serve as good exercise for "basic" trainings. As the book goes on, the reader is more expected to carry out the details. And some of them, although said to be "easy" by the authors, could take some time to figure out.

4. The exercise problems are wonderful. You lose half of the benefits if you don't work out a substantial amount of them.
Many of them are useful results from current research papers, or classical results from these or those "bibles". I myself
haven't done that, and that's why I feel I'm not in the position to give five stars at this moment.

Here's some of my thoughts for an "easier" reading. First, because of the scope of this book, it might be a good idea to read it with real motivations, and maybe during a prolonged period of time. Otherwise you may easily get tired, esp. when you get stuck with some details the authors claim as "easy".
Second, the reading could be frustrating if you care about every detail and do them all alone. A good way would be skipping over some of the details in the first reading, and then coming back at a later time for a second reading, or even a third reading. Find freinds to form a study group would be surely helpful. But I've never had this luck.

Finally, my review is just intended for fellow students. For the opinions of experts, the wonderful review of Frank Knight should be consulted. It can be accessed at MathScinet.

Advanced, but for Revuz and Yor and some friends of their
this book is full of advanced topics, but the authors don't worry about the comprehension of the readers. ... Read more

Book Description

This text provides clearly written and comprehensive tutorials that allow introductory students of statistics to learn how to use MINITAB software to their full advantage. The book's tutorials and exercises demonstrate MINITAB as a way to understand statistical concepts and practice statistical reasoning. All datasets are real and reflect a variety of subject areas, including business, social sciences, physical sciences, life sciences, humanities, engineering, and general interest. ... Read more

Reviews (5)

There is a 3rd edition solution manual!!
I was looking through an old file cabinet in my office and found the solution manual for the 3rd edition of Mood, Graybill, and Boes. It was apparently put together by Boes and published by McGraw-Hill. It is fifty pages long and answers a lot (but not all) of the questions. Perhaps those who have expressed an interest in the manual should contact McGraw-Hill. Take care.

A beautiful text
A beautiful text of mainstream statistics, starting from scratch and taking you clearly to a good standard. If it provided answers to exercises, it would be perfect for self-study. Nevertheless, still amazingly clear without sacrificing the rigour and precision of the maths.
This must be the first book for anyone who wants to take on probability and statistics seriously.

...

Student Solution Manual
The book is very good but if there is a student solution manual to go with it that would be very helpful. The book has very many useful problems but with no solutions i'm never sure if what i did was right or wrong.

I want to take solution
I am studying the book. However it is little bit difficult to solve the example without solution, so I wonder if the solution exist or not. If do, I would like to take the solution. let me know whether it's possible or not.

A request for problem solution manual
I am going to introduce this book as one of two text books for probability course. So I need a problem solution manual. I am wondering if your company distribute the manual. would you let me know how can I get one. I send this message through review because I don't know how to apply for that. Your co-operation in this regard is appreciated. ... Read more

Reviews (1)

Interesting and easy-to-read
I never thought that I would enjoy a math textbook. But this book is written to be really understandable, interesting and even funny! I am not a math person, but I found myself really enjoying Statistics, in large part because of this book. I definitely recommend it! ... Read more

Book Description

Bean's PROBABILITY: THE SCIENCE OF UNCERTAINTY WITH APPLICATIONS TO INVESTMENTS, INSURANCE, AND ENGINEERING is an 'applied' book that will be of interest to instructors teaching probability in mathematics departments of operations research, statistics, actuarial science, management science, and decision science.Comprehensive, easy to read and comprehend, and current, the book uses investment, insurance, and engineering applications throughout as a unifying theme. ... Read more

Reviews (2)

Not a good learning book
Some gaffes in this book, I don't think the author has any real understanding. He even messes up the definition of expectation, and it doesn't get more elementary than that. There are many better probability books out there, don't choose this one.

Excellent
This book is on the course of reading for the Society of Actuaries and Casualty Actuarial Society jointly sponsored Course 1/Exam 1. Although all the probability books listed on the course of reading are good, this text is probably the one to choose. The book seems to be designed with an actuarial student in mind, and an actuarial student would find it very useful for self study. A solution manual is also available.

The quality and level of the writing are excellent. It covers all the required probability topics, and emphasizes certain topics that are not usually emphasized in other texts. Some of these topics are conditional probability; distributional form of Law of Total Probability and Bayes Theorem; conditional expectation; limited moments; mixed probability distributions; survival distributions; hazard or mortality functions; special continuous distributions used in survival analysis (Weibull, Pareto, etc.); compound Poisson and other compound distributions. All but the last chapter on option pricing would be required reading for Exam 1, and the last chapter useful for Exam 2.

A very useful feature of this textbook is that in Chapters 5 (Special Discrete Distributions) and 6 (Special Continuous Distributions) the distribution theory is very clearly outlined. For example, relationship to other distributions, distribution of iid sums, limiting distributions, etc. are clearly stated and summarized. In addition to Exam 1, this text will also be valuable as a reference to study for Exam 4.

Students who would have difficulty with the level of this presentation will also have difficulty with SOA Exam 1. ... Read more

Book Description

Statistics for the Life Sciences presents the key concepts of statistics as applied to the life sciences, while incorporating tools and themes of modern data analysis. The book emphasizes interpretation of results using real data, which facilitates an understanding of statistics and data through the use of graphical data and analysis. The Third Edition has added many new sections to cover probability rules, random variables, the Wilcoxon Signed-Rank Test, and two-way ANOVA and ANOVA for randomized blocks designs. In addition, there is expanded treatment of logistic regression in Chapter 12. This book is an essential statistics reference for professionals and scientists in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.

Reviews (3)

I DON'T HAVE IT YET
I wish I knew where my book was...

easy introductory to statistics
I recently finished using Samuel's book in a dual level statistics course. As an undergraduate I found the material basic and extremely easy to follow. From the perspective of a student who has never had a formal statistics course, I found this book to be simple. Problems are relevant to the life sciences. I perhaps expected more challenging concepts and problems to have been presented-a bit disappointing.

very good stat book
Great examples, great problems, and a great writer sum up this book written by a Purdue Professor. If you deal with statistics and have a career in the health sciences, this is the book for you. ... Read more

Book Description

Building on the foundations of its predecessor volume, Matrix Analysis, this book treats in detail several topics with important applications and of special mathematical interest in matrix theory not included in the previous text.These topics include the field of values, stable matrices and inertia, singular values, matrix equations and Kronecker products, Hadamard products, and matrices and functions. The authors assume a background in elementary linear algebra and knowledge of rudimentary analytical concepts.This should be welcomed by graduate students and researchers in a variety of mathematical fields and as an advanced text and modern reference book. ... Read more

Reviews (1)

A great reference source for advanced matrix analysis
Horn and Johnson's MATRIX ANALYSIS AND TOPICS IN MATRIX ANALYSIS are true classics (like Knuth's Art of Computer Programming). You will find classic theorems and lemmas in matrix theory and linear algebra here along with their proofs (some of these are not found elsewhere).

TOPICS IN MATRIX ANALYSIS contains a lot of stuff including LMI's, Kronecker and Hadamard products of matrices and their properties etc. I found this book indispensible when I was studying Semidefinite Programming.

Both these books are now available in paperback (cost around 30+) dollars each. I have recently purchased both copies and can only strongly recommend them to anyone else. ... Read more

Book Description

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. ... Read more

Reviews (2)

well written
This is a very well written book.Does a good job of reviewing the fundamental concepts and also hitting on advanced topics, has well chosen examples and problems, and is clearly organized and written.
This is a good choice for a computer scientist who is getting into statistics for the first time or needs a refresher. It would also be a very good choice for self study.
The level of this book is approximately that of "Pattern Classification" (also a good book) or the slightly more advanced "The Elements of Statistical Learning" (which I would not recommend).

Buy me now!
Things are easy to find.Well organized and laid out.Great as a textbook and also for reference for other stats classes. And its a great price for a new one!(...) ... Read more

Book Description

Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format. This book grew out of a need to teach bioinformatics to graduate students at the University of Pennsylvania. At the same time however, it is organized to appeal to a wider audience. In particular it should appeal to any biologist or computer scientist who wants to know more about the statistical methods of the field, as well as to a trained statistician who wishes to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, and will be accessible to students who have only had introductory calculus and linear algebra. Later chapters are immediately accessible to the trained statistician. Only a basic understanding of biological concepts is assumed, and all concepts are explained when used or can be understood from the context. Several chapters contain material independent of that in other chapters, so that the reader interested in certain areas can proceed directly to those areas.

Warren Ewens is Professor of Biology at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics, and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceeding of the Royal Society B and SIAM Journal in Mathematical Biology. He was recently awarded the Gold Medal of the Australian Statistical Society and elected as Fellow of the Royal Society. His research interests are in evolutionary population genetics, linkage analysis for human diseases, and bioinformatics.

Gregory Grant is a bioinformatics researcher at the University of Pennsylvania in the Computational Biology and Informatics Laboratory (CBIL), where he has been since 1998. In 1995 he received a Ph.D. in Mathematics from the University of Maryland and in 1999 a Masters in Computer Science from the University of Pennsylvania. His research interests are in bioinformatics in general and in particular in the statistical analysis of gene expression data and significance testing methods for IBD-mapping. ... Read more

Reviews (5)

Disappointing overview
This book is a tremendous disappointment, given other Amazon reviews and the impressive Table of Contents. I picked several topics about which I know something: Likelihoods, P-values, bootstraps. I would have had NO idea about either of these subjects based on the poor delivery in this book. Topics are not well introduced, there are virtually no examples, and the introduction/discussion of most topics is wordy and not informative.

A topic such as the two-sample t-statistic is scattered throughout the book, with the main part not even cited in the index!

Unfortunately there are not a lot of books in the field of Statistics in Bioinformatics. However, I would recommend "The Elements of Statistical Learning" (Hastie et al.) for classifiers etc (Duda and Hart's classic is also good). I would recommend "Biostatistical Analysis" by Zar for a general coverage, and Terry Speed's "stat Labs: Mathematical Statistics ..." which is not comprehensive but has good lab examples with associated statistical analysis.

Pretty good overview
This book is a timely introduction to the mathematical statistics used in computational biology and bioinformatics. The authors have done a superb job in the overview of a subject that students of biology and bioinformatics can rely on for study and for reference. The mathematics is done at an advanced undergraduate level, but the authors are pragmatic in their approach, and interlace the discussion with biological applications immediately after the appropriate mathematical background has been developed. It thus seems appropriate to discuss the quality of the presentation with these applications in mind.

Chapter one begins, appropriately, with an introduction to probability theory, with a consideration of discrete probability distributions of one variable beginning the chapter. The Bernoulli, binomial, uniform, geometric, generalized geometric, and Poisson distributions are discussed. The authors point out the use of geometric-like distributions in the BLAST application. The also caution the reader as to the difference between the mean and the average of a random variable. They then move on to consider continuous distributions, discussing briefly the uniform, Normal, exponential, gamma, and beta distributions. Moment-generating functions are also introduced, and they prove a "convexity" theorem for these functions that is important in the BLAST application. The authors also introduce the relative entropy and generalized support statistics, the later also being used in BLAST.

The next chapter is an overview of probability theory in many random variables. The results in chapter one are discussed in this context, and the authors give an interesting application to the sequencing of EST libraries. The authors also point out that the variance of the maximum of a collection random variables is finite as the number of variables increases, a fact that is used quite often in bioinformatics. Transformations of random variables are also discussed, with the goal of showing how these can be used to find the density function of a single random variable, this also being important in BLAST.

The most important subject of the book begins in chapter 3, wherein the authors introduce statistical inference. They begin with a very brief discussion of the differences between the frequentist and Bayesian approaches to statistical inference and then move on to classical hypothesis testing and nonparametric tests. This chapter is of great value to those readers, for example biologists/would-be bioinformaticists who are approaching statistics for the first time.

Chapter 4 introduces concepts that are of upmost importance in probabilistic computational biology, namely Markov chains. The discussion in this chapter sets up the strategies used in the next chapter on analyzing a single DNA sequence and a latter chapter on hidden Markov models. Shotgun sequencing is discussed as a tool to determine the an actual DNA sequence, and the authors discuss the probabilistic issues that arise in the reconstruction of long DNA sequences from shorter sequences. Missing in this chapter is a mathematical analysis of the advantages/disadvantages between shotgun and whole genome sequencing strategies.

Chapter 6 then generalizes the analysis of chapter 5 to multiple DNA and protein sequences. It is here that one begins to talk about alignments between sequences, which bring about some very subtle mathematical problems in computational biology. The computational complexity of the (global) alignment problem entails the use of softer techniques, such as dynamic programming, which is discussed in this chapter. The (local) alignment problem is also discussed in some detail, using the linear gap model. The alignment problem and the issues with scoring for protein sequences are also discussed in detail. The reader first encounters the famous PAM and BLOSUM matrices in this chapter. The authors do not discuss any connections with the protein folding problem, unfortunately.

The next chapter introduces the basic probability theory behind the BLAST algorithm, namely random walks. They do so with emphasis on moment generating functions, which might be a little abstract for the biologist reader.

The authors return to tatistical estimation and hypothesis testing in chapter 8, with maximum liklihood and fixed sample size tests discussed in some detail. Again connecting with the BLAST algorithm, the sequential probability ratio test is treated.

The authors finally get down to the BLAST algorithm in chapter 9, using an older version of the software (1.4). The connection of the algorithm with random walks and how to assign scores is immediately apparent, as is the ability of BLAST to do database queries against a chosen sequence. The algorithm is compared with the sequential analysis discussed in the last chapter.

The authors return to Markov chains in chapter 10, and give some numerical examples. In addition, they treat the important topic of Markov chain Monte Carlo via the Hastings-Metropolis algorithm, Gibbs sampling, and simulated annealing. An application of simulated annealing to the double digest problem is described. The authors also spend a litte time discussing continuous-time Markov chains.

Hidden Markov models are finally discussed in chapter 11. These have been the most effective tools in sequence analysis and the authors give a nice overview of their construction and properties in this chapter. The Pfam package is discussed as a software implementation of HMMs for determining protein domains. Unfortunately, they do not discuss the excellent package HMMER for implementing HMMs in sequence analysis.

Chapter 12 discusses computationally intensive methods in classical inference. One of these methods, the bootstrap procedure, which is used for large sample sizes, is described. Used to estimate confidence intervals in situations where there is not enough information to employ classical methods, the authors detail a method using quantiles to estimate the confidence interval for the standard deviation of the expression intensity of a gene. This is followed by a return to the multiple testing problem of chapter 3 in the context of the data analysis of expression arrays.

I did not read the last two chapters on evolutionary models and phylogenetic tree estimation so I will omit their review.

guide into the right direction
This is one of the books I have been waiting for. For a population geneticist who wants to learn bioinformatics, most texts are unacceptable: They present heuristic methods in a cookbook fashion, with little reference to what is going on biologically as well as mathematically.

This book is the first exception I know of. It builds, and rests on, solid foundations of genetic stochastic processes and still goes all the way to real-life problems. Let me illustrate this by means of an example, rather than enumerating all the topics in the book.

Chap. 14, entitled `phylogenetic tree estimation' (as opposed to the more common term `phylogenetic tree reconstruction' - not without reason, I presume) builds on, and is firmly interlaced with, Chap. 13 about `evolutionary models', which systematizes the zoo (if not jungle) of substitution models in both discrete and continuous time. On this basis, the overview of tree-building methods makes a lot of sense. Even better, it does not stop here, but presents an application (to real sequence data), followed by a careful analysis of where the various methods agree, and where - and maybe why - they disagree. This way, it clears away some common misconceptions; in particular, it presents a careful analysis of what bootstrap does and what it does not in this context. The chapter closes with a discussion of unresolved problems (like inhomogeneity of substitution rates), and methods and possible pitfalls related to testing of nested and non-nested hypotheses in tree estimation.

The book is written in an informal style without being imprecise, which makes it pleasant reading. It is particularly suitable for teaching at a high level. This is enhanced by realistic (and even real-life) examples that furnish the text, as well as carefully chosen exercises at the end of each chapter.

Certainly, this first edition of `Statistical Methods in Bioinformatics' cannot be the last word in this fast-moving field. But it is an excellent guide into the `right' direction.

poor delivery but potentially useful
The book is written for practicing statisticians who gave a good command of mathematical aspects of statistics. It presents classical topics in statistics (such as statistical inference, random variables and estimation theory) in a flavor of the author's impressions about bioinformatics. It is an excellent idea to present statistics that way. However I also feel that the authors failed to clearly distinguish statistical theory from its specific implementations. Departure from the typical definition-theorem-proof style of mathematical texts is hard on mathematically literate readers for whom this book was written in the first place. It is painful to try to find needed definitions and provable statements in the text even if formulas are numbered. In addition melanges of rigorous theorems with implementations of their consequences make parts of the book devoted to DNA sequence analysis difficult to read.

The authors appear not to have much personal experience with sequence analysis and their exposition seems to be dominated by suggestions from not very honest or objective colleagues. At least that much can be inferred from the list of references given at the end of the book and the content of sequence-analysis-oriented chapters 5, 6 and section 11.3 of chapter 11. On the other hand, chapters 9 (about BLAST statistics), 13 (about evolutionary models), and 14 (about phylogenetic trees) are excellent. Every practicing bioinformatician should read them as a required reading before doing anything with BLAST or with construction of evolutionary trees.

Chapter 12 about computationally intensive methods is also very well written. However, the authors fail to notify the reader that many of the methods (such as bootstrap) have a really bad reputation among researchers