Book Description
Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.
Customer Reviews:
Practical text on extreme values statistics.......2006-11-10
Stuart Coles, who is well published on the open literature, has delivered this practical text on extreme values statistics by providing extreme values theory in a simplified manner with worked examples. Further, Coles has removed much of the complicating aspects (long mathematical proofs and overly complex notation) typical of statistical literature. The text provides any engineer or scientists with the tools required to complete routine extreme values analysis.
A clearly written intro book on extremes.......2002-10-05
I recently used the software accompanied to this book kindly made available by the author and was led to know more about this book and the author's other works. I like what I saw and think the author has done a supeb job in explaining the difficult theory in plain language and in the context of data analysis. Thus it is an "action" book instead of the "just theory" as with most other books. The book provides a balanced treatment of different approaches to extreme value analysis. Personally I prefer the generalized Pareto approach, though theoretically the point process approach may be very neat, if it can be realized.
I think extreme value theory in general is an important statistical area, since in practice one may be forced to deal with analyzing extreme events, such as in financial engineering, environmental or climate analysis, or network design. I wholeheartedly recommend this book for anyone who want to learn this area from one of the leading researchers.
well written with a nice mix of theory and application.......2002-01-29
This book is the most current text available on the theory of extreme values. The author eloquently provides us with an understanding of the theory and it vast applications. It is intended for researchers students and practitioners. So it provides an in-depth account of the theory with many real world examples. It contains an excellent up-to-date bibliography. Important theorems are presented with their implications but without mathematical proofs. Computations are done in SPlus. The author provides an appendix on computational aspects that tells the reader where to go to download examples and find the SPlus functions that are used.
Topics include classical extreme value theory and models, threshold models, extremes in dependent stationary cases, extremes for some nonstationary stochastic processes, the point process approach, multivariate extremes and some special topics including extremes in spatial processes and the Bayesian approach to extremes (with examples employing MCMC methods).
Book Description
This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.
The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added. This second edition comes with a CD-ROM by F. Friedrich,containing a host of (live) illustrations for each chapter. In an interactive environment, readers can perform their own experiments to consolidate the subject.
Customer Reviews:
a bible book to learn Gibbs sampler and simulated annealing.......2000-07-12
This is absolute a bible book for any person who want to learn Gibbs sampler and simulated annealing seriously. The format of this book, though full of mathematical equations, is very self-evident and concise. Nothing is missing and nothing is redundent. It is an enjoyable journey to follow the logic and principle in this book, with all your attention in. There are full of in-depth discussion in all aspect of the Gibbs sampler, simulated annealing, from the visiting scheme to cooling schedule, and parallel algorithms. The references are excellent too. The author seems to have read all publications till 1995 about this topic and give an excellent detailed and in-depth survey in his book. At the end of your reading, you would have love the mathematical form the author used. Without these tools, many discussions in this book will be just impossible and groundless. I personally have read this book for several times.
Book Description
Newcomers to the world of probability face several potential stumbling blocks. They often struggle with key concepts-sample space, random variable, distribution, and expectation; they must regularly confront integration, infrequently mastered in calculus classes; and they must labor over lengthy, cumbersome calculations. Introduction to Probability with Mathematica is a groundbreaking text that uses a powerful computer algebra system as a pedagogical tool for learning and using probability. Its clever use of simulation to illustrate concepts and motivate important theorems gives it an important and unique place in the library of probability theory. The author smoothly integrates the technology with the traditional approach and subject matter, thereby augmenting rather than overpowering it. This book lives and breathes in the sense that not only can it be read and studied in an armchair, but each section also exists as a fully executable Mathematica® notebook on the CRC Web site. Students will find Introduction to Probability with Mathematica an engaging, accessible, yet challenging way to venture into the fascinating subject of probability.
Customer Reviews:
Sample Programs are Available.......2004-12-30
One reviewer said that the sample programs were not available as promised on the publishers website. That may have been true when that review was written, however, I just checked and the sample programs are now there for download on the publisher's website. In fact, I just downloaded them and they are fully functioning Mathematica notebooks.
An excellent book.......2003-12-12
I purchased this book in desperation while taking a probability class with another textbook, and it has been a lucky find, indeed. The mathematics are limited mostly to basic calculus but provide sufficient rigor to satisfy the interests of mathematically-minded readers. The concepts appeal intuitively to the non-statistician scientist or graduate student as well as the mathematician. This book is easy to read and understand. Mathematica enhances the text and aids the homework, but unlike the reviewer below, I believe this book is valuable even without Mathematica. After reading this book I was able to make sense of the assigned probability text and began to enjoy the course. Introduction to Probability with Mathematica was well worth the investment.
A creative and refreshing approach..........2003-11-05
I considered this book for a course that I thought I was going to teach. The course never got offered but I did discover this neat book in the process. Being a Mathematica fan, I was very happy to see a probability book completely based on Mathematica. In fact, the book itself is a set of Mathematica notebooks, making it very easy for the readers to experiment with the introduced topics. The explanations are clear and are accompanied with neat examples showing real-world uses of probability.
There should be more books like this... Really.
........
I was a guinea pig.......2001-01-09
I was one of the test users of this book at the college where Prof. Hastings teaches. With the use of Mathematica (which is assumed in this book), the book allows one to explore the ideas much more easily. By modifying built in commands, the user can get a better grasp on how specific distributions behave. The commands written for the book are also very helpful. I found the book easy to use and the problems ranged from basic to difficult, but most were intersting (particularly the chapter on simulation). For those with access to Mathematica, this book works seemlessly with the program, making Mathematica a simple to use tool to aid in the understanding of probability rather than get in the way.
No software available.......2000-12-27
The book seems to have good intentions.. but... most of the text uses Mathematica Code that is not available... the promised software on the publishers Website is apparently vaporware.. totally unavailable.. and of course the publishers are "not available"... Without software the book is an expensive waste of time...
Average customer rating:
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An Introduction to Statistical Modelling (Arnold Texts in Statistics)
Wojtek Krzanowski
Manufacturer: A Hodder Arnold Publication
ProductGroup: Book
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ASIN: 0340691859 |
Book Description
Statisticians rely heavily on making models of "causal situations" in order to explain and predict fully what is happening. This volume provides a single reference to the subject with an applied slant. It focuses on such core issues as regression, analysis of variance, and generalized linear models. Only the most essential mathematical justifications are given in detail.
Book Description
This general introduction to the mathematical techniques needed to understand epidemiology begins with an historical outline of some disease statistics dating from Daniel Bernoulli's smallpox data of 1760. The authors then go on to describe simple deterministic and stochastic models in continuous and discrete time for epidemics taking place in either homogeneous or stratified (nonhomogeneous) populations. They offer a range of methods for constructing and analyzing models, mostly in the context of viral and bacterial diseases of human populations. These models are contrasted with models for rumors and macro-parasitic diseases. Questions of fitting data to models, and the use of models to understand methods for controlling the spread of infection, are discussed. Exercises and complementary results at the end of each chapter extend the scope of the text.
Customer Reviews:
A Foundation Book.......2007-05-16
This book tells what we knew about the mathematics of epidemics up until 1990. The differential equations (beginning with Bernoulli and moving forward) are presented well. That is, the variables are defined in the text and not too many steps are skipped in the derivations. The high point in this type of epidemiology came in 1927, when Kermack and McKendrick wrote the continuous-time epidemic equations. Diseases were characterized by the parameter rho, the relative removal rate. Up until the 1990s, we were just fitting our data to this model, and estimating rho.
Along came 'computational biology', or 'agent-based models' or 'numerical methods'. After 1990, these new techniques allowed us to escape from the perfect-mixing assumption that caused the Kermack and McKendrick model to depart from reality. With computation, we were able to see the impact of social networks, targeted innoculuations, and to test the value of different intervention strategies. See March 2005 Scientific American. None of those advances are discussed in this book. As a historical treatise, however, it is a superb addition to the library.
Great Service.......2007-02-02
My book arrived before estimated time and in better condition than described. I am a math dork and given the choice I would purchase from this seller.
Excellent Text.......2005-05-14
This is a broad, wonderful introduction to the mathematics of epidemic modeling. The authors have done an outstanding job at pointing out the mathematics of both deterministic and stochastic epidemic models.
Careful study of this small text will prepare one for a serious look at the current research on the subject. This material is far from ``old fashioned" as one reviewer wrote, indeed, this text is a welcome introduction to the subject!
Old-fashioned.......2004-11-04
The authors mainly give an introduction how to do the calculations by hand for several epidemic models. However, some of their tricks for doing the calculations are not very interesting anymore in times where computers are available. More imporatant, they hardly ever explain why they choose a particular model, what they want to calculate and how to interpret the results of the calculations. So reading the book does not give you much insight in epidemic modelling, only in doing some calculation. Furthermore they ignore all kind of recent approaches in epidemic modelling. (Most references are quite old (before 1990) and the few more recent references are most of the time only mentioned without going into detail.)
Average customer rating:
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Introduction to Applied Statistics: A Modelling Approach
J. K. Lindsey
Manufacturer: Oxford University Press, USA
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ASIN: 0198528949 |
Book Description
This text is aimed at students in medicine, biology, and the social sciences, as well as those planning to specialise in applied statistics. It covers the basics of the design and analysis of surveys and experiments and provides an understanding of the basic principles of modelling and inference. Practical advice is provided on how to design a study, collect data, record observations accurately, detect errors, construct appropriate models, and interpret the results. The text contains many illustrative examples and exercises relating statistical principles to research. A companion website is available with links to data sets, R codes, and to an instructor's manual with teaching hints and solutions.
Average customer rating:
- The worst book
- Whomever reviewed this text positively is an idealistic tool
- This book is an embarrrassment.
- Did anyone actually edit this book?
- incomprehensible trash
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Introduction to Scientific Computing: A Matrix-Vector Approach Using MATLAB (2nd Edition)
Charles F. Van Loan
Manufacturer: Prentice Hall
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Contemporary Abstract Algebra.
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First Course in Probability, A (7th Edition)
ASIN: 0139491570 |
Customer Reviews:
The worst book.......2003-11-27
This is probably the worst book in Scientific computation. This book doesnot explain any topic in depth. It's a waste of money to buy this book.
Whomever reviewed this text positively is an idealistic tool.......2002-05-01
Pavlov's dogs could have been trained to write a better textbook on Matlab computing then the author has done. In fact, I believe this book was written and produced by 1,000 monkeys instead of an esteemed Cornell professor. The examples in the book rarely work and I believe that any careful reader will realize that anyone who gives this book a positive review is a complete tool that probably has nothing better to do than to make excuses for his domestic partner's poor job at crafting such an overpriced waste of tree pulp. It seems to me that professors @ Cornell should stay out of the publishing industry because they just can't seem to get anything right. As for the positive reviewers of this text, I believe they should get some of that South African homegrown Viagra "on tap" and have "a thought-provoking and exciting" time for once in their sad sad lives.
This book is an embarrrassment........2002-04-11
I am in the professors class and the lectures are just as incomprehensible as the book. This book uses mathematics to obscure mathematical concepts beyond recognizability. Its unfortunate that this book has wiped out any interest I might have had in numerical analysis. It is not a good reference for anything except for how to write a terrible book. Specifically, the code doesn't make sense and is often inconsistent, the explanations are scanty, typos are abundant, and any knowledge to be had is lost in the muddle. Don't bother with thisbook.
Did anyone actually edit this book?.......2002-02-11
I find it appalling that in a college level textbook there would be spelling errors, problems that just don't make sense (i.e. a problem referring to making four plots but failing to say what they should be of), and untested matlab scripts. This book looks like it was written on a whim and doesn't really cover matlab, it just presents mathematical problems that should be solvable in matlab, but without providing any examples or information in the text to help.
incomprehensible trash.......2001-12-18
This book reads like it was compiled from notes written on the back of napkins by some scatter-brained professor. It lacks any sort of logical organization. Convoluted Matlab code is sprinkled throughout the book without any comments or explanations. Even worse are the long numerical tables of program output that are frequently offered up without any context. Mathematical theory is treated as secondary to the meaningless snippets of MatLab code. Explanations of concepts, when the author is so gracious as to include them at all, are terse and incomplete. This is quite simply the worst textbook that I have ever encountered. It is a disgrace that this book ever made it through publishing. Save your money for something else; you cannot learn mathematics from a schizophrenic cookbook.
Book Description
Introduction to Robust Estimation and Hypothesis Testing focuses on the practical applications of modern, robust statistical methods. The increased accuracy and power of modern methods is remarkable compared tothe conventional approaches of the analysis of variance (ANOVA) and regression. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems withstandard methods that seemed insurmountable only a few years ago. This book provides a thorough, up-to-date explanation of the foundation of robust methods for beginners. It guides the reader through the basic strategies used for practical solutions to problems, and includes helpful updates which are available free of charge via an anonymous ftp site. The book also provides a brief background on the foundations of modern methods, placing the new methods in historical context.
* Covers modern, more accurate methods of statistical estimation and hypothesis testing not covered in existing books
* Provides up-to-date test results dealing with heteroscedasticity
* Software built in S-PLUS is available free of charge via an anonymous ftp site
* Guides the reader through the foundations of robust methods
Customer Reviews:
Great book for applied researchers.......2001-01-07
If you want a useful book on modern ANOVA and regression methods, this is an excellent choice. The book begins with some technical background on robust techniques and then focuses on how commonly encountered problems can be addressed. Included are two-way and three-way designs, split-plot designs, a variety of robust regression methods plus some recently developed exploratory tools. The book also contains a description of s-plus functions that can be used to implement the methods covered. The s-plus functions used can be downloaded for free. S-plus is easy to use and learn and I make use of these functions on a regular basis. If you are more interested in theory than applied work, other books, such as Staudte and Sheather, or Huber, or Hampel et al. will be more interesting. But for applied work, this is the only book I've read that covers many experimental designs that are typically encountered in practice. Even some modern advances related to least squares are covered and can make a substantial difference in accuracy as well as the conclusions reached.
Not an S-PLUS user? Go elsewhere-.......2000-10-31
Dr. Wilcox apparently works copiously but unaccompanied (36 of the book's technical references attribute sole authorship to him). With all due respect to the author, lack of collaboration can be detrimental to what was intended to be a basic technical treatise - and this may be one of those occasions. The author notes (p. 11) "We stress, however, that many mathematical details arise that are not discussed here. The goal is to provide an indication of how technical issues are addressed without worrying about the many relevant details." Indeed, the reader may find this concentration of effort a bit contrary with the introductory nature implied by the book's title. This impression is reinforced by a subject index perhaps too terse for an introductory or reference textbook (being only 3 pages of regularly-sized font, while a less-useful "author index" takes up almost 4 pages).
For the most part, this lean 296 page book is not so much an "Introduction to Robust Estimation" as it is a tutorial or user's manual for numerous S-PLUS functions relevant to the subject matter. If the reader is not heavily invested in S-PLUS (S-PLUS being a high-level computer language and interactive analysis environment trademarked by MathSoft, Inc.), he can never fully appreciate the contents of this title. For example, in the discussion of median variance (p. 42), the author notes that this estimator is related to the beta distribution, but does not acknowledge that there are several related functions that can take this name (such as the incomplete beta distribution, as well as its ratio). Instead, an S-PLUS function 'pbeta' defines what was meant. One must therefore resort to cited third-party references or a computer to really grasp the basics in these situations (in this case only to discover the terminology was inaccurate). A fundamental reliance on propriety software packages and professional journal articles for basic instruction and accuracy is a characteristic unbecoming of an "introductory" textbook, in my opinion.
Often, software manuals tied to specific libraries or languages become dated. I liked the fact that the companion software was downloadable, rather than provided on a medium that might be incompatible with the user's operating system, such as a 5 1/4" disk (the link in the textbook has been updated to www.apnet.com/updates/ireht.htm). For an S-PLUS owner already familiar with robust statistics, this book would probably rate higher. However, of the four textbooks I currently own on this subject, I regret to say that this title only sees infrequent use. A better alternative for emphasizing basic concepts and theory is "Robust Estimation and Testing" by Staudte & Sheather (ISBN 0471855472).
Customer Reviews:
Great little companion!.......2001-05-23
This is a small paperback, but can be of great help when you need a quick and smart reference to basic statistical modelling. You will move from means and averages to ANOVAs in a short time. There are good simple and short explanations, but practical examples are limited by the size of the book...
Book Description
This title deals with the computer simulation of thermodynamic properties of many-body condensed-matter systems that use random numbers generated by a computer in physics and chemistry. It describes the theoretical background of several variants of these Monte Carlo methods and gives a systematic course by which newcomers can learn to perform such simulations and to analyze their results. This third edition has been updated and a new chapter on some important recent developments of the Monte Carlo methodology was added.
Customer Reviews:
A graduate level book (Review for the 4th edition).......2007-03-09
This is a nice little book written by two experts of the field. This edition is only an expanded version of earlier editions (by addition of two new chapters, the core of the book chapter 1 to 3 hasn't change at all). The book covers monte carlo techniques through various well-known examples (Ising model, random walk, percolation, self-avoiding random walk). I enjoyed reading the first 3 chapters of the book. In particular, chapter 3 guides the readers and gives them the chance to practice what they should have learned in previous chapter (through 53 exercises). The following 2 chapters (chapter 4 and 5) are not as nicely written. Moreover, there are some serious shortcoming in the book. (1) All codes are written in Fortran. While everyone who can program can easily understand the codes, Fortran belongs to the past and could have been ok for physics students during late 80's (first edition) but not for those at 2006. (2) The guide (chapter 3) should have been the last chapter and have covered subjects in chapters 4 and 5 (3) As I mentioned before, chapter 4 and 5 are not well-organized. (4) The book in general stresses too much on finite-size effects. However, it is an important subject and it tells us how we can scale our simulation result to more realistic cases. By my judgement, the book gives wrong impression about the degree of its importance.
I recommend graduate students who are serious about learning monte carlo methods to read Newman and Barkema book (Monte Carlo Methods in Statistical Physics) instead since it provides a broader view about the subject. Although I highly recommend those who are interested in the subject to go through chapter 3. It is fun and very instructive.
Books:
- An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman & Hall/Crc Mathematical and Computational Biology Series)
- Applications of Interval Computations (Applied Optimization)
- Applied Multivariate Statistical Analysis
- Applied Multivariate Statistical Analysis
- Applied Numerical Methods with MATLAB for Engineers and Scientists
- Applied Numerical Methods with MATLAB for Engineering and Science w/ Engineering Subscription Card
- Basic Technical Mathematics (8th Edition)
- Bezier and B-Spline Techniques
- Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM
- Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM
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Statistics of Extremes
Modelling Extremal Events for Insurance and Finance (Stochastic Modelling and Applied Probability)
Statistics of Extremes: Theory and Applications (Wiley Series in Probability and Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Extreme Value Distributions: Theory and Applications
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)
