Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science)

"This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples ... In summary, this is book is highly recommended..."
-Biometrics, December 2006

"I enjoyed this text as much as the first one. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R."
–Giovanni Montana, Imperial College, Journal of Applied Statistics, July 2007, Vol. 34, No. 5

". . . well-written and the discussions are easy to follow . . . very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics."
–Andreas Rosenblad, Uppsala University, Computational Statistics, April 2009, Vol. 24

"The text is well organized and carefully written . . . provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike."
–Colin Gallagher, Clemson University, Journal of the American Statistical Association, December 2007, Vol. 102, No. 480

"It provides a well-stocked toolbook of methodologies, and with its unique presentation on these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught."
–János Sztrik, Zentralblatt Math, 2006, Vol. 1095, No. 21