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From the Reviews:
"The book provides many good real life examples to demonstrate application of the methods discussed....[it] is excellent for teaching an advanced class in statistics on this topic as it also contains many good exercises at the end of each chapter, some being extensions of the discussions." (Journal of Biopharmaceutical Statistics (JBS), Issue #5, 2008)
"This book provides a timely and comprehensive review of methodologies for recurrent event data analysis and should be beneficial to Biometrics readers who are interested in recurrent events."
"The strength of this book is its scope. It covers most of the methodology that is readily available for general use. ...Overall, we think this is a very good reference for recurrent event data analysis, especially because no other books provide a similar degree of coverage, and it would provide a nice textbook for a graduate-level course on the topic." (Biometrics, September 2008)
"This book deals with processes generating multiple events over time. … The book comprises eight chapters, four appendices and a useful notational glossary. … it is directed to a much broader target readership, like social scientists, economists and industrial statisticians as well. … Many examples are used to illustrate and discuss the models and statistical methods in great detail. Techniques for estimation, testing and model checking are lucidly described … for a graduate course." (Harald Heinzl, Zentralblatt MATH, Vol. 1159, 2009)
“…Every aspiring statistical researcher interested in recurrent events should have this book on his/her shelf as a great guide for learning the state-of-the-art shastic models, frequentist (mostly estimating equation and asymptotic based) methods, and computational tools (including popular programs and routines). This is a very well-organized and comprehensive book on a very rapidly expanding area of research. As a mentor of PhD students, I myself will definitely recommend every graduate student interested in mastering recurrent events to read this book thoroughly to understand the current state of the literature as well as areas of future research and further development.” ( Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)