Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book. All the data thats used is available in the authors webbsite for downloading, very nice.

To make this review short, I will say that I agree with all seven points made by the reviewer from New York, NY, whomever he or she may be. Franses is clear, concise, authoritative and up-to-date on all the advances.

I particularly like the nice coverage of GARCH models that are new to me. It is a great introductory text especially for economics majors. For more advanced books and other treatments of time series consider Kennedy's fourth edition of "A Guide to Econometrics" or the suggestion from reviewer "New York, NY". Also my listmania list on time series will give you several sources to look at.

Recently, I reread Franses book and expanded my review, which now includes 10 benefits.
(1) Organization by key features of economic time series (trends, seasonality, outliers, conditional heteroskedasticity, non-linearity), rather than by methods, which provides a practical foundation for the various methodologies. The order in which chapters are presented reflects the order of difficulty in modeling trends, seasonality, etc. Even if there were no other benefits, this organization makes it worthwhile.
(2) Appropriate level for first book on time series models as applied to economic time series, explaining more difficult concepts GARCH and VAR without excess detail. Box and Jenksins book is more a textbook; Brockwell and Davis is also more advanced; Hamilton is comprehensive and technical, but not as friendly. This book is very approachable even if you have had only 1 or 2 statistics courses. In economics, many people are interested in forecasting, and Franeses here is a good start. If you are looking for a more advanced forecasting book, try the recent books by Clements and Hendry from Cambridge U Press.
(3) Clear distinction of the steps of model identification, estimation, diagnostics, and selection; something which other time series analysis books do not seem to do early or easily. (4) Delineates stochastic and deterministic models in the second chapter, providing a framework for when to take differences (eg. ARMA vs ARIMA). His timing is excellent. Many people I have interviewed on time series do not understand why they need to difference (eg use prices instead of returns) or why to transform the series (eg use logs instead of actual values).
(5) Generous use of examples with real not simulated data with a website to download all the data, making it possible to import, graph, and analyze on your own.
(6) A website containing printing corrections. Techincal books are likely to have some errors, but very few keep websites to list what those are.
(7) Revealing graphics, especially for conditional heteroskedasticity, the 'CH' in GARCH. Figures 7.1-7.3 illustrate the concept that large returns tend to follow large returns very cleanly.
(8) His notation is clear and consistent, yet not overwhelming: conventional Greek letters, only 1 level of subscripting, matrix noation where appropriate; even the results are neatly presented, as standard errors appear in () below their point estimates. Finally, Franses uses the same notation from chapter to chapter where the term is the same--not so common when chapters written by different authors.
(9) Great appendices: extensive and updated references, a thorough subject index, and an author index. My only suggestion for improvement is that a second edition or the website should contain some exercises. Highly recommended.
(10) The price! There are books published under Wiley at 3 to 4 times the price! under Springer Verlag for 2 to 3 times the price. Certain books are worth the money, but Cambridge University Press paperback publications, when written well, are exeptional values. I encourage the ambitious time series student to look at other time series books, including one written this year by Franses including Quantitative Models in Market Research.

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.

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.

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.

Being a researcher in software metrics, I am really pleased to see a book that is suited for software managers with the correct level of detail in statistics. I particularly enjoyed reading the 4 chapters with case studies. Its a must have for anyone in the field of software metrics and measurement.

This book has a powerful format that blends practical "how to" and common sense with the power and rigor of statistical analysis. I will use this book as a "primer" when implementing software metrics in the corporate arena. This book is a "must have" for anyone implementing a corporate software measurement program. I also wish I had this book in my graduate offerings for Software Development and Design. Existing software curriculums can be sadly lacking the foundations and fundamentals for software measurement and statistics. This book literally makes statistics easy, sensible, and straight forward even for the complexities of software development and technology.

The book provides a solid approach towards dealing with software development project data. It is also written in an easy to understand style although the subject itself is far from easy.
This should provide software development managers with a well founded handle to get more grip on development efforts.

This book provides a good, clear, concise explanation of Cox's proportional hazards models. For someone seeking a non-mathematical description this is a great guide. The original datasets from the text examples can even be downloaded and you can go through the same process yourself. Because of some mistakes in the text, I would recomend looking at other sources as well.

I enjoyed the authors' book on logistic regression analysis in 1989, and this book is just as good, or better, with many extremely practical suggestions on building regression models for survival data. Happily, the authors summarize, compare, and contrast several major texts on survival analysis which have appeared in the past 10 years. For example, they discuss different names used by different authors for score residuals. They present a helpful appendix on the counting process approach to survival analysis, which will make more advanced texts accessible to students; thus, anyone who wants to use survival analysis, at any level, should consult this book, even if he has already studied books by Miller, Lee, Collett, Fleming-Harington,Andersen, et al, etc. An unfortunate drawback to this book is that the first printing contains many careless errors, some of which may affect student learning: for example, the definition of a survival function is misstated. I recommend that you insist on the second or third printing when buying this book, and you will be quite satisfied.

Hosmer and Lemeshow have given us a clear, nontechnical introduction to using survival models. The book strikes a good balance between covering the basics and addressing the most recent, state-of-the-art techniques, including repeated events, frailty models, and others. They also do a good job of addressing practical issues, including estimation details and available software. While most of the examples are drawn from medicine and biostatistics, this book could also serve as a useful starting point for social and behavioral scientists interesting in learning the fundamentals of these models, as well as a useful reference for applied researchers.

This book is loaded with good text book examples and covers a wide variety of bootstrap applications. It is great as a reference book on the bootstrap or as a course text at a graduate level. Chernick (1999) is a little more up-to-date and covers the classifcation error rate estimation problem that is not addressed in this text. Chernick (1999) also has many more references. Efron and Tibshirani (1993) is another fine text that is a little more intuition based with less mathematics. Fieller's problem with ratio estimation and some other gems are well covered in Efron and Tibshirani but not here. Davison and Hinkley do the best job on time series of any of the bootstrap books with details about moving block bootstrap and some interesting applications.

This book has an excellent ballance between practice and theory. It presents the bootstrap as the powerful tool it is through the ellucidation of practical issues. I strongly recommend this book for everyone interested in improving statistical practice.

Relevant and clear in explanations throughout.

What is the best way to learn and be secure in your learning -- work a problem through with expert guidance. This book provides clear advice about what statistics to choose for what problem and then provides small data sets. You can confirm your capability by working the problem step-by-step with the authors -- that includes understanding the meaning of your result and drawing an appropriate conclusion. Students love it. Me, too.

I also used the first edition of this book. Several copies of it in fact. The first two editions were true handbooks that were never out of reach from my desk (unless "borrowed") for over thirty years. I cannot recommend this book too highly to anyone who will be using statistics. May it be as true a friend to you as it was to me.

(But did the price have to increase so drasticly?)

I cut my teeth in statistics with the first edition of this book in 1968, back before we had computer programs to do our statistics for us. And I have kept the second edition on my shelf since 1977. The book leads the reader step by step through the hand calculations for all the basic statistics, and for some relatively obscure ones as well (such as tests of the difference between two correlations or between two proportions). These days, students of statistics go right to their keyboards, and the statistics come out a millisecond or two later. But if you want your students to see how these things are actually calculated, there is no better reference than this.

a) Well organized!
b) Covers a broad range of topics e.g. measure theory, stochastic processes, martingales, Markov processes, stochastic calculus, SDE...and much more!! You can find in it almost all branches of probability!
c) Proofs are short, efficient and interesting, but you have to fill in many details. This gives you a good training!!
d) Results are usually stated in the most general form
e) It requires a strong backgroung in real analysis and functional analysis
e) Very very few typos!

This a compendium of all the relevant results of probability theory; in the words of the author, a book about "everything". Many reviewers and the author himself have pointed out that this work is similar in bread and depth to Loeve's classical text of the mid 70's. I have never read Neveu, but find this book unique. It is not suited as a textbook, as it lacks the many examples that are needed to absorb the theory at a first pass. It works best as a reference book or a "second pass" textbook: Kallenberg's presentation illustrates new aspects of classical topics such as measure theory, martingales, diffusions, point processes, and covers many advanced topics. The author has been able to pack a large amount of information by carefully organizing the material, and avoiding repetitions. Although rigorous and advanced, the proofs are elegant and clear. The goal is not to break the world record for conciseness (that is currently held by Borkar's booklet on probability). Kallenberg begins each chapter with useful remarks, so that the goals are always evident. There are a few typos here and there, but nothing that cannot be easily spotted (unlike Durrett's Probability). Over time, this has become my prominent reference source (and I am using only the first 15 chapters of the book...).

When I was a graduate student at Stanford I took a seminar on point processes taught by Ross Leadbetter who was visiting Stanford for the summer. We used Kallenberg's book "Random Measures". That book provided a concise and mathematically rigorous treatment of random measures. This text on probability is a much larger volume but is masterfully presented.

Kallenberg in his usual rigorous style presents the basic measure theory in the first two chapters. He then covers most of the standard probability theory in the next three chapters. Random variables and processes are covered in chapter 3 with the concepts of convergence and independents and the important zero-one laws. Probability distributions, expectations and higher order moments are also covered in chapter 3.

Chapter 4 deals with random sequences and series and averages and includes the strong law of large numbers and Kolmogorov's three series theorem. Chapter 5 covers characteristic functions and important limit theorems including the central limit theorem (Lindeberg-Feller version).

Conditioning and coupling are covered in Chapter 6 and martingales, submartingales and optional stopping are also covered. Upcrossing inequalities and maxima are also discussed here.

Stochastic processes are covered in chapters 8 - 10 and point processes in chapters 11 and 12. Chapter 13 introduces Gaussian processes and Brownian motion. The law of the iterated logarithm is presented in chapter 13 also. Chapter 14 deals with the important Skorohod embedding technique and invariance principles.

The remaining 13 chapters cover many advanced ideas including convergence of random processes and measures, stochastic integrals and Ito calculus, Feller processes and semi-groups, ergodic theory for Markov processes, stochastic differential equations, diffusions, semi-martingales, large deviations, connections with partial differential equations and more.

This book contains every topic I have seen in texts on advanced probability and more! Kallenberg tends to be both rigorous and elegant in his presentation.

This book is for graduate student and probabilists and mathematical statisticians who need these tools to establish limit theorems. It is not intended for an undergraduate course in probability for non-mathematicians. It requires an understanding of advanced mathematics.

I found this book accessible when I first read it in 9th grade, and I still find it fascinating today as a soon-to-be grad student in math.

I planned to do a talk on the subject of the mathematics of a particular game, called Snood, and I had to learn Game Theory quickly to do so. This book explains things well, and the exercises, while easy enough to do in my head, still cement everything very well so that I can honestly say that I have a solid understanding of the subject even though I just picked up a single book.

Very good.

I found this book to be a very enjoyable read, covering the most interesting ideas in game theory and how they have impacted on other sciences from biology to sociology.
Almost no mathematical knowledge is required, because the text focuses on the ideas not the math.
Even if you want to learn about Game Theory including the mathematical foundation, I recommend to read this book first. It will wet your appetite for Game Theory and show the breath of ideas and applications.

I'm an undergraduate student for electrical engineering in Tel Aviv University. I find this book very interesting and fun to read. However, I must say, that it has a serious lack of examples, and there aren't any exercises. In bottom line - it can be an excellent book for professionals, as a student ? it's almost impossible to study from it without the lecture notes.

This book makes great reading. There is a fair amount of (well written) high level mathematics, but also a number of sections of a more historical or narrative nature, and a wonderful sense of humor pervades the work. The account of the laying of the transatlantic cable in the nineteenth century and the technical problems associated with it is priceless. Several sections are devoted to the life of Fourier. There is also a companion volume entitled ``Exercises for Fourier analysis''.

Wonderful book! Fourier was a man of immense and multivariate talent. His main mathematical discovery shares this property of him: besides its beauty and depth, its applications arise practically everywhere. The book of Korner is both rigorous and delightful. A very rare combination.

FDA is a very important new topic in statistics and Ramsay and Silverman provide an accessible introduction to the topic.

Functional data occur when the data are curves. For instance, we might monitor growth of children sampled at a fairly fine grid over several years. Or we might consider reports of experienced pain in many patients over a fairly long period of time. Even when the data *seem* discrete (and given measurement error and a finite sampling rate all data really *are* discrete) there may be substantial advantages to treat them as continuous.

Functional analysis extends the notion of linear space that is the foundation of statistics to the infinite dimensional case. In a infinite dimensional space, a matrix equation becomes an integral equation, and so on. They provide a useful introduction to the topic, enough that a non-specialist can get into it. The big difference between this treatment and older ones is that Ramsay and Silverman emphasize that the data generating process is assumed to be continuous. Many older treatments of similar data involve no curve regularization or smoothing. Basically they ignore the underlying continuity. Ramsay and Silverman show there are substantial benefits to paying attention to the continuity. For instance, if we want to estimate the derivative of a sampled curve it's logical to use first differences. They demonstrate, however, that fitting a smooth to the curve, e.g., a spline, and then finding the derivative of the smooth curve often does a much better job. (Why? Differencing amplifies noise.)

Anyway, they cover topics of linear models, principal components, canonical correlation, and principal differential analysis in function spaces. Their general feel is fairly exploratory. The one thing this book is short of is long examples, which can be found in their companion volume Applied Functional Data Analysis.

Bernie Silverman is a great writer. Once again he has written a very accessible book on an interesting but difficult topic. Functional data are series of curves. These kinds of data are often treated under the topic of longitundal data analysis and of course they can also be put under the general category of mutlivariate analysis. Because the x axis often represents time you may also view the analysis of these data as falling in the category of multivariate time series.

Jon Ramsay is a professor of psychology who has contributed to the research in multivariate analysis and has a lot of experience with important applications of functional data analysis. He has had many major publications on this topic in leading statistical journals and has made advances in curve registration and in the development of principal differential analysis.

What is exploited in the functional data analysis approach is the treatment of families of such functions through basis functions (wavelets, Fourier series, orthogonal polynomials etc.). The canonical example is a group of adult males whose growth curves are under study. Each curve has a similar shape but each individual has some differences in the asymptote and other parameters of the curve. Defining these parameters, chosing the approximating functions and assessing the fit to the data are all part of art of functional data analysis.

Silverman is an expert in smoothing and kernal density techniques and you will see his expertise and research contribution exhibited in this text. The roughness penalty approach is one method covered in this book and in more detail in a Chapman and Hall monograph with Green.

Registration of curves is a particular technique that is unique to functional data analysis. Other techniques discussed in the book are generalizations or extensions of existing multivariate techniques such as principal components and canonical correlations.

Shape and smoothness of a curve can be described through derivatives and so differential operators play an important role in functional data analysis. It has a chapter devoted to it and another chapter on a technique called principal differential analysis.

The book concludes with a forward looking chapter on the future of functional data analysis and the challenges that remain ahead.

Also look at the fine review on amazon by dataguru who emphasizes the exploratory aspects of the approach presented in this text and the need to have some knowledge of spline functions.

This book deals with statistical analyis of multivariate data which may be treated preferably as curves. Examples of such situations include multivariate time series data which are observed at unequally spaced intervals, and two-way data in social sciences, and many high-dimensional data. Since this is the first attempt at a systematic account of this rapidly growing area, it wisely chooses to focus on descriptive and exploratory techniques developed by the authors and others. The readers are well-advised to have some background on smoothing spline which is employed as the key modeling framework.

For curious readers like me, it still leaves more to be desired. For example, the theory is better prepared by Grenander (1981)'s Abstract Inference, while the practice is preceded by the vast work on analysis of space-time field (4-D var) in climate research using EOF, similar to the principal components, but applied to the 2-d field data. I would also like to see more discussion of alternative modeling techniques such as wavelets and kernel smoothing methods.

I find this book a handy reference, so would recommend to others for the same purpose.