This book is basically an update of their 1997 mongraph. Longitudinal data are important in biostatistics and particularly in the analysis of clinical trials. There are effective methods for handling longitudinal data using linear models with covariance structures that represent the time dependence of the repeated observations. There are many subtle issues in the analysis and many who analyze longitudinal data apply incorrect linear models and are often not aware of the consequences of their decisions. The authors were motivated to provide a reference source to remedy this problem. The book presents the theory and applications and uses SAS Proc Mixed as a vehicle for presenting many of the results in a clear and understandable fashion. An important feature of the book is its emphasis on how best to deal with the problem of missing data. This is covered in chapters 14 - 16. Although SAS is emphasized throughout the book other software tools are also illustrated in Appendix A (including SPlus). SUDAAN is a package produced by the Research Triangle Institute in North Carolina that also handles longitudinal data but is overlooked by the authors. Another great book on longitudinal data analysis is Diggle, Liang and Zeger "Analysis of Longitudinal Data" published in 1994. There have been many advances since 1994 and Verbeke and Molenberghs cover a great deal of it. You can find my review of Diggle, Liang and Zeger on Amazon. An updated second edition of their book is in the works and will probably appear in 2001.

I think that it is a really good monograph about the logit and the probit models. It is very accessible at the appropriate level. I made a great deal use of it. Although it has a introductory review section about the linear regression model, having a good understanding of it, and also of statistics, is necessary in order to understand the rest of the book well.

Seber pays meticulous attention to detail. Not only does he present a comprehensive text on regression, but his precise, straight-forward language separates this work from other books on the topic. This is a "must-have" for any serious statisticians library.

Ron Christensen is an excellent writer who has written many intermediate to advanced level texts on linear models, multivariate analysis, time series and categorical data analysis. They all provide a good mix of theory and applications and cover topics often neglected in the standard text books including Bayesian approaches to inference. This book is no exception.

Christensen's emphasis is on log linear models much in line with the earlier text by Bishop, Fienberg and Holland (the text he learned from). However Christensen attempts to create a balance by not being quite as advanced as Bishop, Fienberg and Holland or Haberman or Santner and Duffy but not as introductory as Fienberg or Everitt. Also Christensen's text is much more current than many of these texts that were published in the late 1970 through the 1980s.

The second edition has added more material on logistic regression and logistic regression has even been added to the title. The other major change in the text is the addition of chapter 13 on Bayesian binomial regression. Chapter 12 on likelihood theory for categorical data is taken from Christensen's linear models book.

Like Hosmer and Lemeshow this book includes many examples and illustrates the use of various available software packages. It differs in that it covers more theory and emphasizes log-linear models whereas Hosmer and Lemeshow deal strictly with logistic regression.

Many people favor Agresti and view his text as the bible on categorical data analysis. Christensen's book os a worthy competitor. A unique feature is the inclusion of graphical models, a topic rarely covered except in specialized texts such as Edwards. Christensen also covers the Bradley-Terry model for paired comparisons, a very useful model for ranking players or teams in sports, bridge or chess tournaments. He also shows how the modeling fits into the generalized linear model framework. He also addresses the improtant distinction between fixed and random zeros in contingency tables.

The similarities and differences between categorical and continuous data are made and issues of variable selection in regression modeling are addressed.

When I took a job to model prediction of loss reserves for workers compensation insurance, I began to realize that the traditional statistical methods that I generally relied n would not help me (without modification). The required modification would be either to transform variables or to model long-tailed probability distributions. This is because in the insurance business you have to reserve for those big catastrophies. The cost data for workers compensation data generally show a high frequency of low to moderate costs... . However occasionally there are a few cases of sever injury causing permanent disability which could run over 1 million dollars. Even though the probability of occurrence is small the cost is so high that it cannot be ignored. Such claims will surely be found when large insurance company cover millions of employees over many years.

The problem occurs when insuring for floods, earthquakes, fires and other disasters. Stuart Klugman and Bob Hogg in 1984 wrote the first introductory text to acquaint statisticians with such probability models that are important in the insurance business. Other books covering the subject were covered in books on risk theory designed for actuaries. This book covers all the topics and assumes mathematical and staistical knowledge at the level of the book by Hogg and Craig (so some calculus is required).

"The book is very prescriptive and full of lists and tables with which to guide managers in making effective decisions in using computer-assisted clinical trials in pharmaceutical studies." (Technometrics, Vol. 45, No. 1, February 2003)

Once you use Maple, you'll never approach math problems in the same way again! With Maple you can solve and plot equations, create 3-D animations, and create technical documents. At Bakersfield College we offer a 5-week online course in how to use this amazing software. If you're interested, please contact me.

This is the most readable introduction to Markov processes I have found.

This thorough and thoughtful book emphasizes critical thinking through careful exploration of Probability. Hands-on, minds-on group activities combine with effective classroom lessons to create a wonderful unit plan. I taught a 4 week unit to a class of third graders using this book as my guide and the class didn't want to stop learning probability!