I read this book, cover to cover, after flipping through looking at the pictures. What a great read! I will never look at a chart again, without a newfound (and critical ) eye towards the graphical representation of the information. I am also glad to see the author writing for the on-line e-zine IntellectualCapital -

I heard Howard Wainer speak at a conference last week, and I am ordering his book sight unseen. His intellect, wit, and humor in front of the group was amazing. If the book captures half of that, it will be a treat.

Nelson is a private consultant who has worked on countless practical problems in reliability from his consulting practice and previous employment at General Electric. He is an excellent lecturer and writer. His earlier book "Applied Life Data Analysis" was considered to be one of the best texts on reliability.

This book is very thorough in its treatment of all aspects of accelerated testing and is filled with many good references. Nelson carefully defines the mathematical models which consist of two components, (1) an acceleration function which describes how the mean lifetime changes as a function of the acceleration factor and (2) a probability distribution that explains the random variability of outcomes at each acceleration factor. A particular mean function could be the Arrhenius relationship and the probability distribution could be exponential. Hence there is not a single Arrhenius acceleration model but rather an Arrhenius-exponential, an Arrhenius-lognormal or an Arrhenius-Weibull model. The book is filled with interesting theory and examples. Nelson provides excellent practical guidance based on his wealth of experience.

This is a fantastic book on advanced sampling techniques of rare, clustered, populations. The reader should be familiar with basic cluster sampling methodology. The authors develop the theory behind the techniques, and illustrate the methodoly with good, insightful examples.

There are not many books on Advanced Mathematics written specifically for Statisticians. Prof. Khuri's book aims to do just that, covering not only advanced calculus but also linear algebra and matrix theory as well. So it is more comprehensive than its title suggests. There are excellent annotated bibliographies at the ends of chapters, making it especially valuable as a reference work, and also a set of exercises. The only omission is a separate chapter on matrix differential calculus; perhaps this will be included in the next edition. It would also be nice to have solutions to problems provided. Recommended.

I'll begin by noting the unintended humour on Amazon's part, the Preface is signed Derek Richards, Milton Keynes, January 2001, by Derek Richards of England's Open University. Milton Keynes, named for John Milton and John Maynard Keynes, is, of course the location of the Open University. Amazon credits the preface to Milton keynes. Not the first work that Milton Keynes has been credited with, certainly not the last.

Anyway, to the book. Books on mathematical methods for physics have been lagging behind technical innovations. This book introduces Maple in the first three chapters and then uses it extensively in chapters that begin with functions, series and limit, and ranges through most topics in differential equations to dynamical systems. I would have liked to see an introduction to symmetry methods and Lie groups as they are particularly easy to implement on computer algebra systems. But then again the book is already long at 862 pages. Anyway, this book is a must have for working physicists and applied mathematicians. A good text for advanced undergraduates and beginning graduate students. Many solutions are available through the author's web site.

This book gives the basic essentials of stochastic processes in a rigorous yet very readable way. The standart subjects (Discrete time/continuous time Markov chains, renewal processes, Poisson processes) are treated in good detail. Some more advanced subjects (Brownian motion and general random walk) are also given.

The presentation is very organized, and the author gives many interesting (and mostly, fun to read) examples. The introductory material in chapter 1 is very detailed. Chapter 2 on discrete time MC's is excellent. (I especially enjoyed seeing the interplay between convergence of time-averages and distributions).

This book is obviously more advanced than some other texts in this area (e.g., S. M. Ross - Stochastic Processes), and it emphasizes the technicality behind the proofs a bit more. I think that after learning the basic ideas in some undergrad level course, it is easier to read. One drawback of the book is that in some places the treatment is a bit dry (e.g., in the beginning of chapter 3 - Renewal processes).

Overall, I give it 4.5 / 5 stars.

This is an excellent elementary introduction to numerical analysis, only basic math is required. It is fun and easy to read. This is a "small" book; the largest section (linear equations) being 66 pages. However, it does cover a lot of ground.

Code fragments are in C and FORTRAN. The C code obviously hasn't been tested (abs() instead of fabs() throughout). There are many typos in the text as well as in the code fragments.

This book is a classical book in data analysis. It provides techniques and methods for effectively analyzing non-standard or messy data sets that arise from experimental design situations. You can always be benefit from the book for your whole life

The author is one of the pioneers in the newly developing field of multivariate survival analysis. His work goes back to his Ph.D. dissertation in the mid 1980s. These methods come into play when one is studying more than one survival curve and the event times are correlated rather than independent. Practical applications include situations when multiple events are studied on the same patients, such as time until contracting the disease, followed by time to complications and then possibly by time to death from the disease. Studies involving events related to twins can also be analyzed by these methods.

He gives an excellent exposition and a number of good examples. He provides the reader with a very current list of references from the literature.

The author presents the four common approaches to the problem and concedes that the field is in its infancy. He believes that while some of the methods described will prove not to be as fruitful as others, at this point it is still difficult to determine which are the most promising. His aim is to expand the toolbox for researchers in medical and biological fields who have experience with univariate survival analysis and may be faced with multivariate problems. He covers such important current topics as fraility models and competing risks.

In my opinion the author has succeeded in his goal and provided biostatisticians with a reference source that will be useful to them for many years. It should not be your first book in survival analysis though. See the book by Lawless or Kalbfleish and Prentice before attaching this book.

This is an elegant and well-written classic. Scheffe's proofs are clear and straightforward, without being too terse. His use of orthogonal bases for vector spaces makes proofs of independence and derivations of distributions easy to understand. _The Analysis of Variance_ has definitely stood the test of time.