In a very simple and concise approach, explains the essentials with complete theoretical background and good illustrating examples. Will be very useful for both the novice and the advanced reader.

This book is well written and includes many practical examples and illustrations. It provides Minitab macros for computations and includes modern advances including the bootstrap. These macros are written out in an appendix and the data sets used are also listed in an appendix. The data and macros can be used on the computer throught the floppy diskette that comes attached to the book. The authors show exact calculations of the bootstrap distribution for the median. This is useful text that I referenced in my book on the bootstrap. The level of the book is intermediate and is appropriate for an advanced undergraduate or a graduate course in modern statistical inference. It covers both the robust estimation of location and scale and has a nice bibliography of the literature as of 1990. It also includes many advanced topics such as the two-sample problem, robust regression and time series. Chapter 1 introduces modern advances in statistics and provides motivation for robust methods. The authors emphasize the importance of computing in modern statistics but also provide a sound warning against the over-reliance on computers. They make a serious effort to mix theory and practice, covering the important asymptotic results and theoretical concepts and also providing practical examples mainly from biology and the physical sciences.

This book presents 4 kinds of ruin models: Compound Poisson, Sparre-Anderson, Markovian models (including markovain regime-switching and periodic risk model) and model with premium depending on reserve. It assumes a strong background in matrix, measure theory and stochastic processes like Markov process and renewal theory and is not elementary compared with the books by Hans Gerber or Jan Grandell.

The beauty of this book is that the proofs are short and the ideas are very well presented and motivated. Unlike the book "stochastic processes for insurance and finance", which presents much more models and tries to be elementary but makes all the proofs unbearably long and messy (this book has no figure!), the author makes very good use of figures to illustrate excursion, time reversion and phase-type distribution and links many aspects of ruin thoery to queneing theory. The only two problems I found is that the notations used in the book is quite different from other books about risk theory and one of the eleven chapters in the book contains quite a number of typos.

This is the second edition of a text on survey sampling with a different title than the first edition. The first edition published in 1973 was based on a short overview course that Barnett gave on survey sampling. It's title was "Elements of Sampling Theory." The second edition published in 1991 was intended to serve a similar purpose but update the book with advances over the intervening 18 years.

The text is 163 pages including the postscript chapter. The six basic chapters cover an introduction to the methods of sampling from finite populations along with the wide range of application areas in Chpater 1, ideas and properties of simple random sampling in Chapter 2, data collection method for carrying out a survey in Chapter 3, ratio and regression estimates in Chapter 4 including the Hartley-Ross estimator, stratified sampling in Chapter 5, and cluster and multi-stage sampling in Chapter 6. This certainly covers the basics and Barnett the reader to the well-known works of Cochran, Kish, Groves and Kalton for additional details.

This book is clear and concise, much like Silvey's monograph on statistical inference. Like Silvey this is a handy reference book.

As a marine ecologist and a person with a non-statistical background this book was hard going but is worth the effort 10 times over. I have been using this book for more than 10 years now and finally decided to buy a copy for myself. I would recommend this book to anyone that has to design sampling programs for field surveys in ecology. Beware however - it is not for the faint hearted or those who do not have a bent towards statistics or numerical analyses.

This is the fourth edition of this primer on linear models by Littell and Freund. As with the previous editions it gives a thorough treatment of the models and their application through the use of SAS procedures. In the fourth edition, Walter Stroup has been added and Phil Spector dropped as coauthor. Also this edition drops the word "System" in the title after "SAS". The main addition to this edition is the inclusion of a lot of detailed information on the application of mixed effects models through use of the MIXED procedure. There is a great deal of comparison of the differences between PROC MIXED and PROC GLM in the analysis of these models. Many interesting examples are presented and care is taken to show the user how to specify the models so as to get the appropriate analysis. They also teach you enough to aid in interpreting the output.

Littell, Milliken, Stoup and Wofinger have also written a very nice book titled "The SAS System for Mixed Models" and my only question would be to ask "which book offers more?" This fourth edition seems to now cover many of the same topics that highlight that book.

Readers should be aware of the two books and should investigate for themselves the differences and overlap before deciding to purchase either one. One clear difference is the date of publication. This book, published in 2002, is more current and has several references from 1997 and after whereas the mixed models book was published in 1996.

There is not enough reference-book for SAS Programmers. But this book is very concise, clear-cut and literally "effective".

When I was in trouble because of SAS programs' performance, this book helped me out of performance trouble.

I changed my programs according to book's advice. And I got almost 5 times higher performance.

This book is best among SAS programming guides.

For the ones who don't like Statistics it's a good book to start with something apparently confusing. Actually, Statistics is dismistified in this book. In the first part of the book it's given a introduction about the basic things that SAS can do as a statistical program. The second part of the book is dedicated to the Statistics accompanied with the SAS language, once given in the first part. Good real examples give exactly the notion what SAS can do about a Statistical Analysis. It's also a very good book for the ones who aren't good in English, as myself (i'm brazillian). If you are planning to study Statistics with some computational help try to start with this book.

Gustavo Tavares If you want this review translated to Portuguese send me a request.

An truly wonderful treatment of the statistics of scans (clusters of events in some window of time and/or space). The first half of the book walks through applications & formulas, the second half adds hard-core details, proofs, etc. It is well written and contains many good examples with copious references to primary literature.

This book is far better than Runs and Scans with Applications by N. Balakrishnan and M. Koutras, which I judge to be an effort to impress the reader with how well the authors can write convoluted
statistical proofs rather than actually teach the reader something useful, a tendency in statistical texts which I despise.

Great book that explains the statstics concepts very well. Great book.