Recursive Partitioning and Applications: (0 Springer Series in Statistics)

The routes to many important outcomes including diseases and ultimately death as well as financial credit consist of multiple complex pathways containing interrelated events and conditions. We have historically lacked effective methodologies for identifying these pathways and their non-linear and interacting features. This book focuses on recursive partitioning strategies as a response to the challenge of pathway characterization. A highlight of the second edition is the many worked examples, most of them from epidemiology, bioinformatics, molecular genetics, physiology, social demography, banking, and marketing. The statistical issues, conceptual and computational, are not only treated in detail in the context of important scientific questions, but also an array of substantively-driven judgments are explicitly integrated in the presentation of examples. Going considerably beyond the standard treatments of recursive partitioning that focus on pathway representations via single trees, this second edition has entirely new material devoted to forests from predictive and interpretive perspectives.For contexts where identification of factors contributing to outcomes is a central issue, both random and deterministic forest generation methods are introduced via examples in genetics and epidemiology. The trees in deterministic forests are reproducible and more easily interpretable than the components of random forests. Also new in the second edition is an extensive treatment of survival forests and post-market evaluation of treatment effectiveness.

Review :
STATISTICAL METHODS IN MEDICAL RESEARCH "The beauty of the Zhang and Singera (TM)s book is that it gives an excellent comparison between conventional regression models and recursive partitioning techniques. This comparative approach gives the reader insight into how a recursive partitioning technique can have an advantage over the conventional methodsa ]Overall, the book provides an excellent introduction to tree based methods and their applications. It can be a good place to start learning about recursive partitioning. In addition, biostatisticians will enjoy the real life examples that have been used in the book."