The Statistical Analysis of Recurrent Events (Statistics for Biology and Health) - Tapa dura

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)

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the modeling assumptions that they are based on. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies are all covered, with procedures for estimation, testing and model checking.