Book Description

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics. ... Read more

Book Description

This classic book retains its outstanding ongoing features and continues to provide readers with excellent background material necessary for a successful understanding of mathematical statistics.Chapter topics cover classical statistical inference procedures in estimation and testing, and an in-depth treatment of sufficiency and testing theory—including uniformly most powerful tests and likelihood ratios. Many illustrative examples and exercises enhance the presentation of material throughout the book.For a more complete understanding of mathematical statistics. ... Read more

Reviews (13)

book I used at Maryland
Hogg and Craig is one of my favorite texts. It is an intermediate text in mathematical statistics similar to Mood, Graybill and Boes. I took qualifying exams in mathematics for my Masters Degree in math at the University of Maryland in the early 1970s. One of the exams I took was in statistics. I had little formal training in statistics at the time. Hogg and Craig was the recommended text for the statistics exam. So I bought it and studied out of it on my own. It was very clear with excellent coverage of methods for deriving distributions for random variables and transformations of random variables. I passed my exams and got my highest grades on the statistics exam even though I had more training in abstract algebra. Hogg and Craig really helped. It has been revised since then to maintain currency with statistical developments but it still has maintained its clarity and usefulness.

Excellent in its day - but that day has passed
At the time of its debut (1958) this book was viewed as a fairly advanced treatment of probability and math stat. While it has been updated over the years, it has not kept pace with the rapid changes in the field. (Craig died in 1978!). For those with a bit integral of calculus (it is a math stat book), it is good for review and has some excellent problems (but any answers in the back of the book are simply numeric answers with no explanations). For the right class, it still could be a really good intro to math stat. Be warned - this book gives you nothing in the way of measure and probability but if you can buy it used and cheap, it is well worth having.

Old school textbook.
One of those little monochrome bound textbooks from the 1960's that you wish had never been written (i.e. the book explains statistical methods using mathematical jargon and symbols).

Unless you are a math major, who communicates in these terms, steer clear.

Not a good book.
This book is currently required for my intro. to stats course. I have found this book to be of no help at all. It breifly discusses the subjects with little or no structure; requiring you to use other references constantly.(Never a good idea in math where notation is almost never consistent!) Theorems/definitions are unclear and un-highlighted. Possibly the most frustrating aspect is the examples. Examples are given without even posing a question and the "solutions" to the examples are done with very little explanation or methodology. Also, the examples seem to be much easier than the excersises which have only a limited number of answers in the back of the book. I feel the text does not come close to preparing the reader for the excercises. As another reviewer suggested, this book could be useful to someone with experience and background in statistics, but for an introductory course, the book leaves out too many background details that it assumes the readers know. I am sure there are much better introductory books.

Introduction to Mathematical Statistics by Hogg and Craig
I worked with an earlier version of this text. The text is
geared for math majors. There are many practical examples;
however, the more theoretical examples are elusive at best.
Coverage of the basic laws of probability is good. The examples
dealing with continuous random variables require some prior
knowledge of intermediate calculus which should be no problem
for math majors or engineers. The book benefited me when I
took and passed the Fundamentals of Engineering Examination.
In addition, I've taught statistics several times.
This work is geared for the above-average student. In some
areas, it could be supplemented with the Schaum's Statistics
Outline. ... Read more

Book Description

From the reviews of the First Edition.

"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."
—Choice

"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."
—Contemporary Sociology

"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."
—The Statistician

In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet. ... Read more

Reviews (4)

update of very well written and popular text
Hosmer and Lemeshow point to the massive growth in applications of logistic regression over a ten year period from the time of publication of the first edition of their text. They found over 1000 articles that used logistic regression during that time frame. There also have been many software advances that make it easier to apply logistic regression. The authors do their computing mostly in STATA. But they also acquaint the reader with many other useful standard packages for applying logistic regression. They also provide a web site from the publisher where data sets can be found.

New topics include the use of exact methods in logistic regression, logistic models for multinomial, ordinal and multiple response data. Also included is the use of logistic regression in the analysis of complex survey sampling data and for the modeling of matched studies.

The book is intended for a graduate course in logistic regression requiring the student to be familiar with linear regression and contingency tables. Similar in spirit and objectives to the first edition, this text also maintains the clarity of thought and presentation that these authors have a history of providing.

This is an important update to the first edition and is worth having on the bookshelf in any biostatistics library. I have my own personal copy and I think many others would also benefit by having it as a reference.

Should suit the needs of most, especially analysts
This is an excellent beginning and intermediate text on logistic regression analysis. Avoids the thorny details, but provides a wealth of references for those who are interested.

Anyone who is serious about doing logistic regression analysis should have this book.

highly regarded text on logistic regression
This is a very popular and well written text on logistic regression. The topic is very useful to biostatisticians. Hosmer and Lemeshow have taught some short course out of the text which have been well received. The authors are knowledgeable and thorough. The book is very much oriented toward real applications and does not require advanced mathematics.

A valuable tool for the applied statistician.
This book is widely considered the "bible" of logistic regression analysis. It provides an accessible introduction to the theory of logistic modeling, and gives in-depth coverage of the proper use of the method, including interpretation, diagnostics, and practical considerations. Indispensable for anyone who uses logistic regression in their work. ... Read more

Book Description

Written for introductory statistics courses found in departments of mathematics, business, social science, and educational two and four year schools. High-school algebra is the only prerequisite. Statistics: A First Course is written in an informal style without omitting the more difficult concepts. Sanders utilizes an intuitive presentation that encourages students to use a common sense approach to data analysis. Over 2100 problems drawn from health care, business and economics, the social and physical sciences, engineering, education, and leisure activities demonstrate the wide range of questions that can be answered with the help of statistical analysis techniques. To enhance the appeal and readability of the text, scores of minicases, STATISTICS IN ACTION, are found in the margins. These minicases provide examples of "real world" statistical applications and can be used to stimulate class discussions. Additionally, the material on probability has been extensively revised and updated and a timely chapter (Ch. 10) on Quality Control has been written for the new edition. Examples showing how computer statistical software packages may be used to eliminate computational drudgery and support analysis and decision-making efforts are integrated throughout the text. A Computer Exercise section at the end of each chapter gives students further practice in using statistical software. ... Read more

Reviews (1)

You don't need to be afraid of Statistics
This is a very friendly statistics book.It is easy to read and understand.The authors also throw in some humorous comments now and then that is something you wouldn't expect to find in a statistics book and this seemed to help with the reading.Many real world business examples are used in the book.The example problems and the end of section or chapter problems are from different areas of study such as business, political, social sciences, and science. This is great for understanding how to apply statistics in real life.I would have to say that all the topics and formulas are explained very well.In some cases there are more then one way to write a formula and Sanders and Smidt makes a point of giving you both formulas.You can decide which formula works for you.The book comes with a CD loaded with the data files for all the problems so you don't have to spend time keying in data.There are copies of the data files come in a variety of formats such as Minitab, Excel Windows, and Macintosh.For most of the example problems the book takes you through the steps using Minitab Statistical software and the TI-83 graphing calculator.You are not only learning statistics, but you are also learning how to use Minitab and the TI-83 graphing calculator.In this day and age if you are going to do statistics you need to be able to do it with a computer.I wish they would have also included examples using Microsoft Excel, which more people are apt to have then Minitab. Personally I use both Minitab and Excel. I actually enjoyed reading this book and looked forward to studying.Even though my statistics class is over this book will not be collecting dust for a while as I plan on going back through it to reread or finish reading what I missed or skimmed over. There are still a lot of the problems that I want to do to reinforce what I have already learned.This is a great statistics book to keep as a reference. More then once I have found coworkers looking up stuff in my book. ... Read more

Book Description

Stata is a powerful data analysis software. This handbook was designed to bridge the gap between textbooks and Stata's own documentation. In this intermediary role, STATISTICS WITH STATA uses easy to follow tutorials to demonstrate how to use Stata to accomplish some of the most common statistical tasks. While Stata's user documentation is over 4,000 pages, this tidy manual is just 400 pages, and introduces students and practitioners to both basic and advanced features of Stata. ... Read more

Book Description

David Howell's practical approach focuses on the context of statistics in behavioral research, with an emphasis on looking at data before jumping into a test. This provides students with an understanding of the logic behind the statistics: why and how certain methods are used rather than just doing techniques by rote. Students move beyond number crunching to discover the meaning of statistical results and how they relate to the research questions being asked. FUNDAMENTAL STATISTICS FOR THE BEHAVIORAL SCIENCES contains an abundance of real data and research studies as a base and moves through an analysis of data. ... Read more

Reviews (6)

A clear and comprehensive introductory statistics text
I have been using this text for Levels 1, 2, and 3 social sciences statistics teaching in Britain and abroad for over 8 years now. Although I have tried using other introductory texts, I have yet to find a text as comprehensive and as clearly written as this.

The worst stats book for the social sciences
I hate to be so critical, but I must agree with my colleague who described his/her harrowing experience with this textbook. I have two rules about learning statistics: no one is ever above a review of the most basic topics and an author/professor can never go wrong by providing a variety of examples within the same topic area.

superbly-written and serious intorduction
I've been using the 3rd edition for several years now, and I just think it's an extraordinarily clear, concise, and well-written book. Howell is better at presenting the basic statistical concepts (of ANOVA, for example) than any other author I've seen.

Other introductory books (such as Runyon's "Fundamentals of Behavioral Statistics") may provide more advanced treatments or cover more material, but in general they end up being long-winded and unfocused. Howell's book is crisp.

And in case you're wondering about the title, statistical methods for the behavioral sciences are not different than statistical methods in any other quantitative discipline. It's just that Howell draws his examples from psychology, sociology, etc., and may make mention of some of the conventions used by researchers in these fields.

A rare jewel
This may be the single best textbook I have ever used, or it may just seem that way because of the difficulty most stat textbooks have in explaining their concepts. Unfortunately, I only discovered it after two semesters of incoherent text books and bad teaching; it was only later, through his book, that I got the entire picture. Howell starts with the "why?" of statistical tests (necssary, but often not done), takes you through the equations relatively painlessly, and provides realistic commentary on actual uses, strengths and weaknesses, and controversies surrounding statistical techniques. I now tutor in statistics (really), and I just rely on Howell for the simplest and best method of explication. Now, if only he wrote an advanced text...

An outstanding introductory text in statistics.
This text is the most clearly written and useful of four different introductory statistics texts that I have examined. Its emphasis on insight rather than rote learning of formulae makes the subject easily understood and retained.

This book is a "keeper".

The author stresses the use of computers discusses several statistics programs available. He also thoughtfully provides exercise data for input to several a computer program. It is unfortunate, however, that he failed to mention the most powerful of these programs on the market today. I hope that he will examine this in future editions. ... Read more

Book Description

This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It's packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives.
Each copy of the book includes an e-Text on CD - that is a complete electronic version of book. This e-Text features enlarged figures, worked-out solutions, links to data sets for problems solved with a computer, multiple links between glossary terms and text sections for quick and easy reference, and a wealth of additional material to create a dynamic study environment for students.
Suitable for a one- or two-term Jr/Sr course in probability and statistics for all engineering majors. ... Read more

Reviews (7)

Worst book ever
I have never seen as many mistakes in any other college level textbook in my life. The authors are so lazy, the end of section questions refer to tables and information which you have to flip backwards 50 or 75 pages to find the example they are referring to. The could not come up with new examples of what. Definitely use Bean's book which uses many real world examples to illustrated ideas. This book is awful.

Riddled with Errors
This book has the potential to be good but I've already uncovered about 20 mistakes during 2 weeks of extremely light use. There are numerous problems that are set up so that the answers are insane (Z > 9 problem 7-42). I think the publisher should fire whoever proofed this book. Don't waste your money, get Walpole and Myers instead for an introductory text.

There are so many mistakes...so many
There are quite a few unforgivable mistakes...They highlight one of the most important formulas on a page and the formula is WRONG... The answers at the end of the book are so wrong that will confuse you. The authers should proofread the book much more carefully.

Good for reference
This is an excellent reference work, though, as a text is is inadequate. I found that several of the answers in the back are incorrect and often the practice questions rely too much "refer to problem 8-16." The explanations are somewhat hard to follow as is the practice of placing figures some distance away from the related text. This is a Wiley book so it is over-priced, but what are you going to do?

a MUST for every working engineer
I took this class during my 1st semester as an IOE student at Univ of Michigan. Back then I never had done any real applications of Stats before, so I basically chugged through the course. But now as a professional, working engineer, I realize what an awesome book this is!! It is a MUST as a reference tool for ALL engineers.. great examples as well. DON't sell this book back!! ... Read more

Book Description

When it comes to pinpointing the stuff you really need to know, nobody does it better than CliffsNotes. This fast, effective tutorial helps you master core statistical concepts – from numerical measures, probability, and sampling to principles of testing, univariate inferential tests, and bivariate relationships – and get the best possible grade.

At CliffsNotes, we're dedicated to helping you do your best, no matter how challenging the subject. Our authors are veteran teachers and talented writers who know how to cut to the chase – and zero in on the essential information you need to succeed. ... Read more

Reviews (7)

Excellent
I am taking Stat 244 after taking the first class 5 years ago. I needed a review to catch up, but I am finding that this is much more comprehensible than the textbook and the instructor. Nice and straight forward... Makes you wonder why you need to spend $80+ for a used textbook in college when you can learn it more efficiently from this little guy... THANK YOU!!!!!!

Get Cliff's Quick Review of Statistics
Cliff's Quick Review of Statistics is a good supplement
to any Stat course, and should be kept as a reference.
Good intro text. It ought to be required.

For non-quantitative concepts, presented for the layman,
get the classic "How to Lie With Statistics" by Huff.

Good condensed review
This book is a condensed review of statistics, without laborious examples. It can be used as a refresher or supplement to a more verbose introductory primer. It is perfect for a high school or a one semester introductory stats course.

Statistics Made Comfortable
One of the best summaries of basic statistics: this is how I see this brief book. It gets straight to the point without unnecessary verbiage and irrelevant exercises cooked up by authors of some enormous volumes on statistics that serve to confuse and intimidate rather than teach. This book won't give you detailed explanations of statistical concepts, but there is enough here to supply any beginner with a lot of good understanding of basic statistical techniques, and the book will give an intermediate user a nicre refresher course.

Good for a partial review
This book reviews the basics of "descriptive" statistics very well. Because of this, it is great for the intro stats student. Although it touches on regression, it does not cover the analytical areas very well.

So, if you only have that high school or one semester stats course to get through, buy this book. ... Read more

Book Description

A comprehensive, exceptionally well-written text that instructors find to be appropriate for students with varying levels of mathematical skill, ranging from those suffering from math phobia and anxiety to those adept at math. The text provides students a first look at social statistics by illustrating the application of statistics to contemporary social issues. Students learn to read and interpret statistics in a variety of settings. Each chapter is complete with numerous practice exercises, problems, and demonstrations and applications of the latest SPSS statistical software package to reinforce chapter concepts, operations, and formulas.Students can also find demonstrations and applications using the MicroCase statistical package on the text's companion web site. The book's primary emphasis is on developing the following skills necessary for students to become "statistically literate": computational competence; appreciation of statistics; and the ability to read professional social science literature. ... Read more

Reviews (2)

There is much to be desired
The book covers most necessary areas of stats, but does it not in a brilliant way: 1)In persuit of making stats simple, the author seems to have left out crucial information in some places so that it leaves the reader confused - you understand HOW to make calculation, but not WHY you should do them. 2) Writting could have been clearer and consise. Examples aren't really well selected - some much more lucid and compelling ones could be brought up to illustrate the points made. 3) The book promises "a wide range of student resources" on its web site - in fact there is nothing there except a review of elementary math. 4) The lay out of the book isn't bad, but this merit is outweighted by the mess in referencing. E.g. Index indicates that info on t-distribution is on p.46 - there is not a single word about t-distribution there, whereas all the info is on pp. 197-199, which is not mentioned in the index at all; etc.

In general, if you just want to pass an exam in elementary stats and can do with rudimentary knowledge of the subject, go with this book. For real understanding of statistics look at different books.

Stats made simple
I have done poorly in statistics before but this book seems to be laid out with good clarity and is better than the first book I used.

This book is part the required texts for Sociology 210 at Loyola Marymount U. in Los Angeles.

This book has a handy layout map that allows the person learning or teaching to circumvent areas of the topic that are not of significant bearing to the class.

I am just starting so take my 5 star rating with the caviat that I am not too familiar with the text yet. Amazon is cheaper than my School book store though!! ... Read more

Book Description

The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a comprehensive text and reference work on scientific computing.Thoroughly self-contained, it proceeds from mathematical and theoretical considerations to actual, practical computer routines.This new version incorporates completely new C++ versions of the more than 300 Numerical Recipes Second Edition routines widely recognized as the most accessible and practical basis for scientific computing, in addition to including the full mathematical and explanatory contents of Numerical Recipes in C.Key Features:

Book Description

An easy to understand guide to matrix algebra and its uses in statistical analysis. Presents material in an explanatory style instead of the formal theorem-proof format; the only background necessary is high school algebra. The self-contained text includes numerous applied illustrations, numerical examples, and exercises. ... Read more

Reviews (1)

Definitively a classic
This is an excellent introductory book, although mostly oriented to people with a reasonably good mathematical background. Searle covers most, if not all, the matrix algebra that you may need to fully understand applications of mixed linear models. Chapters one to eight cover the essential operations (addition, multiplication, determinants, etc) as well as more advanced concepts (rank, canonical forms, inverse, etc). The rest of the book (chapters nine to fifteen) covers applications to statistics as well as advanced topics that can help you to understand how do some of the statistical packages work inside. The only mathematics book that I have read from cover to cover. Definitively a classic. ... Read more

Book Description

* Concise and complete coverage of probability.
* Thorough coverage of regression modeling, design of engineering experiments, and statistical process control.
* Wide range of problem levels. ... Read more

Reviews (2)

helpful
This book helped me to solve many types of engineering problems which require an appreciation of variability and some understanding of how to use both descriptive and analytical tools in dealing with variability.

Full of errors. There are better books.
This book is a terrible statistics book. It is full of inconsistancies and mistakes. Believe me there are much better books out there. ... Read more

Book Description

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management, and this trend will only accelerate in the coming years. Unfortunately there is a large gap between the limited treatment of portfolio construction methods that are presented in most university courses with relatively little hands-on experience and limited computing tools, and the rich and varied aspects of portfolio construction that are used in practice in the finance industry. Current practice demands the use of modern methods of portfolio construction that go well beyond the classical Markowitz mean-variance optimality theory and require the use of powerful scalable numerical optimization methods. This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book.

"For money managers and investment professionals in the field, optimization is truly a can of worms rather left un-opened, until now! Here lies a thorough explanation of almost all possibilities one can think of for portfolio optimization, complete with error estimation techniques and explanation of when non-normality plays a part. A highly recommended and practical handbook for the consummate professional and student alike!"

Steven P. Greiner, Ph.D.

Chief Large Cap Quant & Fundamental Research Manager

Harris Investment Management

"The authors take a huge step in the long struggle to establish applied post-modern portfolio theory. The optimization and statistical techniques generalize the normal linear model to include robustness, non-normality, and semi-conjugate Bayesian analysis via MCMC. The techniques are very clearly demonstrated by the extensive use and tight integration of S-Plus software. Their book should be an enormous help to students and practitioners trying to move beyond traditional modern portfolio theory."

Peter Knez

CIO, Global Head of Fixed Income

Barclays Global Investors

Reviews (1)

Packed with diagrams that make learning easier
As a teacher of basic statistics, I am always trolling for the best book to use. However, there is a repetitive sameness to many of them. Therefore, when I encounter a new issue, it is often more an act of duty than one of interest when I examine it. Fortunately, I found this one to be impressive. The exposition is clear and there are many examples worked out in complete detail. A large set of problems is given at the end of each section and a more detailed set at the end of the chapters. These problem sets include a self-review test, mini-projects and a set of problems to be solved via a TI-83 programmable calculator or a computer using Excel or Minitab.
The characteristic that stands out on first examination are the number of pictures and diagrams used. Students at this level need quality diagrams, as their visual sense is generally far superior to their ability to perform mathematical abstractions. I am always puzzled when I open an introductory statistics book and do not see a large number of diagrams. The coverage is normal, so in that area, there is nothing to differentiate this book from any other in the field.
Sometime within the next two months I will have to make a decision regarding which book to use in statistics next academic year. While that decision is not yet final, this book is certainly on my short list from which the choice will be made. ... Read more

Book Description

This introductory text provides a clear understanding of the intuition behind derivatives pricing, how models are implemented, and how they are used and adapted in practice. M. Joshi covers the strengths and weaknesses of such models as stochastic volatility, jump diffusion, and variance gamma, as well as the Black-Scholes. Examples and exercises, with answers, as well as computer projects, challenge the mind and encourage learning how to become a good quantitative analyst. ... Read more

Reviews (4)

Good book on the basics
This book comes in between Wilmott and the more technical books.
And its by no means complete, if you want a more comprehensive treatment you may want to buy wilmott.
And if you need something more technical you should
get the book by Oskendal and/or Nielsen.

If you want to get an inexpensive book then go for this.

An outstanding book in a crowded field
In recent years bookshelves (and readers) have groaned under the weight of new First Courses in Mathematical Finance. There is, of course, a huge overlap in content and it is no easy task to write a book which is both better than its predecessors and genuinely novel. In both tasks Mark Joshi has succeeded admirably: this book deserves to become the leader in its field.

Finding the right level of mathematical sophistication is a difficult balancing act in which it is impossible to please all readers. Here, the author has had a clear vision that the principal audience is the practising or potential quantitative analyst (or quant) and writes accordingly; it is impossible to do better than taking an approach of this sort. Such a quant must have a certain minimum level of mathematical background (a good degree in a numerate discipline). By definition, this has to be assumed for a decent understanding of the material, but the author always has an eye on what a quant really needs to know. Integrated into this mathematical work is a good deal of information about how markets, banks and other corporations operate in practice, not found in more academically-oriented books.

The first half of the book includes the core material found in any decent first course on the subject including basic stochastic calculus, pricing of European options through discounted expectation under a risk-neutral measure, the Black-Scholes differential equation and so forth. Where this book really stands out, however, is the exceptional clarity with which the key concepts are separated. Not only are three different ways for deriving the Black-Scholes formula presented (through PDEs, expectation, and the limit of discrete tree-models) ; much more significantly, the different roles played by hedging, replication and equivalent martingale measures in enforcing a price are made crystal clear. In whatever way you already think about this material, you will almost certainly come away with something new from reading this treatment. In my case, for example, I gained a much greater understanding of why "risk-neutral" pricing is so called.

The second half of the book, roughly speaking, covers a selection of more sophisticated material. The major areas covered include interest-rate derivatives and models; and more complicated models for stock price evolution (such as stochastic-volatility, jump-diffusion and variance-gamma) that have been proposed to correct inadequacies in the Black-Scholes model such as its failure to explain market smiles. Once the core ideas have been so thoroughly explained in the first half, a great deal of interesting and diverse material can be covered rapidly yet with a great deal of clarity and coherence, relating the new models to core ideas such as uniqueness of prices and hedging issues.

Those with quantitative finance experience are still likely to find a good deal that is new and worthwhile in this book. And if you a thinking about becoming a quant, I cannot think of a better book to read first.

Most comprehensive
This is the most comprehensive and up to date textbook on quantitative finance that I have seen so far. Joshi is an excellent mathematician and an excellent quant. He knows finance like the back of his hand, and explains it very well.

A must read for anyone interested in mathematical finance
The modern paradigm within mathematical finance is the use of martingale
methods for the pricing of options; an understanding of it is
critcal not only to quants who use these mathematical tools on a day
to day basis, but also to risk professionals in general when understanding the
risks inherent in a new product. At present, however,
there are very few accessible texts that discuss this at a level that
is suitable for the (sizeable) interested audience; texts either do not
have adequate coverage of the martingale methodology, concentrating on the
older less insightful pde methods, or concentrate (too much in the
reviewers opinion) on mathematical rigour and
require a substantial understanding
of probability theory before one is able to understand and appreciate
the finance.

Mark Joshi's book fills this niche admirably: it is mathematically rigorous
where it needs to be, but more importantly "physically" insightful --- the
author takes considerable pain in assisting the reader in developing
an intuition both for the models used and the products that are
priced. However, the mathematics is all there; more importantly
for the finance professional there are details on how to implement the
various models described. Again in marked contrast to other texts available
the book includes a number of relevant exercises (with solutions) and
computer projects --- features which this reviewer welcomes.
The book is also to be applauded on the fact that
it does not end after a discussion of the Black Scholes stock case ! Instead
the second half of the book discusses, admittedly assuming a slightly higher
level of mathematical sophistication (but never beyond, what one would
expect of a good physical sciences/mathematics graduate), multiasset options,
the LIBOR market model, stochastic volatility and jump diffusion models.
This again is a key strength of the text, rendering these subjects far
more accessible to a wider audience.

In short this is a book which anyone who is interested in mathematical
finance should have on their book shelf. ... Read more

Book Description

Highly praised for its clarity and great examples, Weiers's text takes an informal, student-oriented approach to fundamental statistical concepts. Non-technical terminology is used to describe statistical concepts, which are presented in the context of contemporary applications and student experience. Realizing that many business students are intimidated by this course, Weiers provides numerous learning aids and interesting applications drawn from real-world experience common to many students. ... Read more

Reviews (1)

The best statistics book ever
Finally a statistics book that isn't just a math book with no practical implication.

This book really helped me at my job to make decision calculations. ... Read more

Book Description

This book takes a rigorous approach to, and therefore creates a deeper understanding of, the usual topics handled in one-dimensional calculus--limits, continuity, differentiation, integration, and infinite series. The text was designed to bridge the gap between intuitive calculus courses normally offered at the undergraduate level and the sophisticated analysis courses offered at the senior or graduate level. The author wrote the book with two goals in mind: the development of a rigorous foundation for the basic topics of analysis, and the less tangible acquisition of an accurate intuitive feeling for analysis. ... Read more

Reviews (6)

Excellent Introduction
I believe the author has given students a wonderful text for the beginning of analysis.I disagree with the student who stated that the proofs are unclear.When I took this class last fall, all of the proofs were not clear in the beginning but with a little clarifying by my professor, they quickly became understandable.The problems at the end of the sections are designed to help students fully understand the chapter and while some of the problems require a lot of extra work, they are not impossible.It seems to me if you gave this book a bad review, it was because you were unwilling to put forth the time and effort to fully understand the material.This book was extremely helpful and guided the class in a clear and concise manner.

Extremely unclear
As a math major, I am of course interested in the subject of Real Analysis. But I cannot agree with the other reviews in that the book is very UNACCESSIBLE to undergraduates! I have poured through the first 3 chapters until I almost have them memorized and the concepts are NOT CLEAR. Instead of explaining what is going on, the author rambles on about the "beauty" of his subject and how "we" as students should "carefully" digest the ideas. The examples and proofs are NOT clear and are NOTHING like the HW problems! In fact the proofs are so incomplete, that proving them in the fashion that the author does, does not convince my professor??? This book is a BIG waste of over $100 and a class that I might have enjoyed if the author could see outside of his ego and stop trying to show off to undergraduates who do not know the subject yet!

Great book by Great Guru
In my college days ,i found Edward D gaughan's 'Introduction to Analysis',amidst pile of other books.I read the preface countless times.With his words of encouragement and admonishment ,writer makes you more comfortable ,in your long journey ofMathematics which is real and abstract at once!! I liked this book;After a decade still i fondly remember this book !!I recommend this book whole heartedly, to any one who is sincere in his approach and wants to become first rate mathematician.

Excellent
At first glance, the book appears too thin.But once you've delved a few pages into it, you immediately recognize that the author has simply mastered the art of brevity - while remainining eminently readable.The book covers all the material needed for an undergraduate analysis course - and covers it extremely well.The proofs are easy to follow and extremely instructive, so that the book can also be used in a "Bridge to Advanced Mathematics" or "How to do Proofs" that some schools offer to their math majors to ease the transition from problem-based lower-division courses to proof-based upper-division courses.

A Good, Rigorous Introduction to Analysis
I used this book for an undergraduate course in Analysis and was pleased with it. It is well-organized with poignant examples. The physical construction of the book, however, proved not to be as good as the contentand it is quite worn. ... Read more

Book Description

Introduction to Probability Models, 8th Edition, continues to introduce and inspire readers to the art of applying probability theory to phenomena in fields such as engineering, computer science, management and actuarial science, the physical and social sciences, and operations research. Now revised and updated, this best-selling book retains its hallmark intuitive, lively writing style, captivating introduction to applications from diverse disciplines, and plentiful exercises and worked-out examples.

The 8th Edition includes five new sections and numerous new examples and exercises, many of which focus on strategies applicable in risk industries such as insurance or actuarial work.

The five new sections include:
*Section 3.6.4 presents an elementary approach, using only conditional expectation, for computing the expected time until a sequence of independent and identically distributed random variables produce a specified pattern.
*Section 3.6.5 derives an identity involving compound Poisson random variables and then uses it to obtain an elegant recursive formula for the probabilities of compound Poisson random variables whose incremental increases are nonnegative and integer valued
*Section 5.4.3 is concerned with a conditional Poisson process, a type of process that is widely applicable in the risk industries
*Section 7.10 presents a derivation of and a new characterization for the classical insurance ruin probability.
*Section 11.8 presents a simulation procedure known as coupling from the past; its use enables one to exactly generate the value of a random variable whose distribution is that of the stationary distribution of a given Markov chain, even in cases where the stationary distribution cannot itself be explicitly determined.

Other Academic Press books by Sheldon Ross:
Simulation 3rd Ed., ISBN:0-12-598053-1
Probability Models for Computer Science, ISBN 0-12-598051-5
Introduction to Probability and Statistics for Engineers and Scientists, 2nd Ed., ISBN: 0-12-598472-3

*Classic text by best-selling author
*Continues the tradition of expository excellence
*Contains compulsory material for Exam 3 of the
Society of Actuaries ... Read more

Reviews (17)

Well written, but tough.
This book is not intended for beginners in the field. Yet this book is suitable for those who have had solid background in mathematical statistics and calculus. Also, many of the examples and excercises are difficult to solve. Hence, I would recommend this book to be used as a textbook.

I love this book!
The first four chapters alone (intro, random variables, conditonal probability, markov chains) are worth the price of the book. The author packs each chapter with very interesting examples and problems. The one I found most interesting was his probabilistic analysis of the 2-SAT and SAT problems of computer science. Here he gives an informal math argument as to why 2-SAT is polynomial time decidable and why SAT should be intractable.

On the other hand, I think someone relatively new to probability theory may find his neat problems and examples a bit too much with a first reading. The book is in its seventh edition, and I think Ross has taken advantage of this by providing newer insights and more interesting problems, but in doing so it may overwhelm the novice.

If you are learning probability for the first or second time, I recommend you supplement this book with Roussas "A Course in Mathematical Statistics". Despite its title, the first 9 chapters give a calculus-based intro to probability. And the rest of the book is *excellent* for a calculus-based intro to statistics.

Much examples
This is a good book for a senior undergraduate probability course, it has many examples which provides good understanding. But some results I think it would be too difficult for a 3rd year course to have their students to absorb the material. Again, it is pretty pricy too.

This book is great but required background
Some readers found this book hard to read. This is due to the misleading title, i.e., the word 'introduction'. This book will be great only if the readers have basic concepts of probability. It is suggested to read the book 'A First Course in Probability' by the same author or similar kind before reading this book.

This book is great but required some background
Some readers found this book hard to read. This is due to the misleading title, i.e., the word 'introductory'. This book is great only if the readers are prepared. It is suggested to read an introductory book like "A First Course in Probability" by the same author or similar kind. ... Read more

Book Description

This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data.Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification.For experimental scientists in a variety of disciplines. ... Read more

Reviews (12)

A Students Review
First: I must prefix this by saying that I am majoring in the Mathematical and Computer Sciences.

This semester I decided to take a class that happened to use this text as its source. I have been extremely pleased with it: the theoretical work is excellent, the proofs are thourough, the exercises are both good and cover a broad variety of difficulties, and the tables on the CD provide excellent experience in analyzing real world data.

A couple of things to keep in mind before you purchase this book, however:

1) A good background in linear algebra and basic statistics is highly recommended and virtually necessary to interpret this book. Remembering the knowledge gleaned from "Sequences and Series" (often taught in Calculus II) will also prove useful. The text is good, but it is often nontrivial.

2) Some kind of software that does multivariate analysis (and if nothing else, will find eigenvalues and orthonormal eigenvectors) is necessary to get the most out of this book. The software package SAS is touched on in the book, but by no means is given a comprehensive review. However, the data files on the CD-ROM should be loadable by any competant software package, so use the one you are most comfortable with.

If not overly familiar with any of them, I can recommend S, SPlus, and "GNU's S" (also known as "R") for their power and flexability to work with the data presented in the book.

All and all I found this to be an excellent book, definantly worthwhile if you want or need to know how to do multivariate analysis.

A precious text
This is a very good, very understandable book. It could (maybe should) be used as a first course in multivariate techniques. Concepts very well explained, it needs no more than a good basic course in statistics to be fully absorbed. It also includes enough matrix algebra to be self-contained.

not good
Your typical stat book written by a typical stat professor: dull, dry, and boring and geared towards other professors instead of people who might actually want to *learn* something.

This book is a paradox: it is geared for those who want to learn about multivariate statistics, but is written for those who are very experienced with multivariate analysis, espicially mathematical applications.

Why an instructor would ever assign this book is beyond me; buy and tolerate this book for class if you must. In fact, I don't know one student in my multivariate class who did not complement, supplement, and eventually supplant this text with another text, exasperated by the authors' pretentiousness and unnecessarily complex explanations.

Broad and practical
People complain that statistics is about memorizing a bunch of formulas and when to use them. I disagree. The real problem is that the formulas almost never apply exactly to the subject at hand - they have to be adjusted to each application.

That's why this book is so helpful. It gives huge numbers of results I can use immediately, but also shows me where they came from. That means that I can rephrase the formulas as needed in special computing environments, but still be sure that I'm getting a meaningful answer. The book has a secondary emphasis that I value very highly: checks that the techniques are giving meaningful answers. It's pretty silly, and perhaps dangerous, to apply a technique without knowing how good its results are. This book gives me the checks I need to measure the quality of the results from each technique.

No, there's no C code to cut and paste. This is a math book. The math is clear and well-developed, though, and mostly limited to linear algebra. As I type this, I have the book open to "Discrimination and Classification," and I have my protoype C program on the screen. That's how directly applicable the book is.

I admit, I haven't appplied every technique in the book. All of the book is equally well-written, though. When I need principal components or basic clustering, this is the book I'll grab first.

Complicated.
Why is nearly everything associated with upper division math and statistics seemingly difficult? I took a class that used this book, and some how got a B, I can't even remember half the stuff in this book. Why can't someone write a book that is simple to use, simple to understand, and a little more realistic! Don't get me wrong, I love math, I just have never sat down in a job and did a math proof. Proofs just seem to make the subject for difficult. I want to use the material in a realistic setting, maybe a real life setting?! ... Read more

Book Description

Praise for the First Edition of Design and Analysis of Clinical Trials
"An excellent book, providing a discussion of the clinical trial process from designing the study through analyzing the data, and to regulatory requirement . . . could easily be used as a classroom text to understand the process in the new drug development area."
-Statistical Methods in Medicine
A complete and balanced presentation now revised, updated, and expanded
As the field of research possibilities expands, the need for a working understanding of how to carry out clinical trials only increases. New developments in the theory and practice of clinical research include a growing body of literature on the subject, new technologies and methodologies, and new guidelines from the International Conference on Harmonization (ICH).
Design and Analysis of Clinical Trials, Second Edition provides both a comprehensive, unified presentation of principles and methodologies for various clinical trials, and a well-balanced summary of current regulatory requirements. This unique resource bridges the gap between clinical and statistical disciplines, covering both fields in a lucid and accessible manner. Thoroughly updated from its first edition, the Second Edition of Design and Analysis of Clinical Trials features new topics such as:
* Clinical trials and regulations, especially those of the ICH
* Clinical significance, reproducibility, and generalizability
* Goals of clinical trials and target population
* New study designs and trial types
* Sample size determination on equivalence and noninferiority trials, as well as comparing variabilities
Also, three entirely new chapters cover:
* Designs for cancer clinical trials
* Preparation and implementation of a clinical protocol
* Data management of a clinical trial
Written with the practitioner in mind, the presentation assumes only a minimal mathematical and statistical background for its reader. Instead, the writing emphasizes real-life examples and illustrations from clinical case studies, as well as numerous references-280 of them new to the Second Edition-to the literature. Design and Analysis of Clinical Trials, Second Edition will benefit academic, pharmaceutical, medical, and regulatory scientists/researchers, statisticians, and graduate-level students in these areas by serving as a useful, thorough reference source for clinical research. ... Read more