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Recursive Partitioning and Applications
 
 
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Recursive Partitioning and Applications [Hardcover]

Heping Zhang (Author), Burton H. Singer (Author)
4.5 out of 5 stars  See all reviews (4 customer reviews)
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Review

STATISTICAL METHODS IN MEDICAL RESEARCH

"The beauty of the Zhang and Singer’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 methods…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."

Book Description

This book describes the recursive partitioning methodology and demonstrates its effectiveness as a response to the challenge of analyzing and interpreting multiple complex pathways to many illnesses, diseases, and ultimately death. For comparison purposes, standard regression methods are presented briefly and they are applied in the examples. We emphasize particularly the importance of scientific judgment and interpretation while guided by statistical output. This book is suitable for three broad groups of readers: 1) Biomedical researchers, clinicians, public health practitioners including epidemiologists, health service researchers, environmental policy advisers; 2) Consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients' problems; and 3) Statisticians interested in methodological and theoretical issues. The book provides an up-to-date summary of the methodological and theoretical underpinnings of recursive partitioning. It also presents a host of unsolved problems whose solutions whould advance the rigorous underpinnings of statistics in general. Heping Zhang is Associate Professor of Biostatistics and Child Study at Yale University. In addition to the methodology and application of recursive partitioning, he is interested in developing statistical methods for analyzing correlated data, especially family and genetic studies, and brain imaging problems. Burton Singer, a member of the National Academy of Sciences, is Professor of Demography and Public Affairs at Princeton University. His research interests include combinatorial formulation of randomness, infectious disease epidemiology, and bio-demography of aging.
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4 Reviews
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4.5 out of 5 stars (4 customer reviews)
 
 
 
 
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5.0 out of 5 stars sequel to CART, April 28 2001
By 
Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews
(REAL NAME)   
This review is from: Recursive Partitioning and Applications (Hardcover)
Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning. There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.
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5.0 out of 5 stars Recursive Partitioning in the Health Sciences, Jun 13 2000
By 
Boyd D. Collier (La Mesa, CA United States) - See all my reviews
This review is from: Recursive Partitioning and Applications (Hardcover)
Zhang and Singer have done a splendid job of explaining recursive partitioning, a topic that should be of great interest to anyone who wants to make sense of data in which there are many potentially important variables contributing to some outcome or variable of interest. One should not be put off by the "... in the Health Sciences" part of the book's title; the potential audience of readers who can benefit from reading it is much greater than this implies (I'm an ecologist, for example). Why? First, because the topics covered have wide applicability in many fields; and second, because the writing is exceptionally clear and easy to follow. If you are able to use a typical introductory text on multiple regression, for example, you should have no difficulty getting a lot out of Zhang and Singer. If you are able to handle a mathematically rigorous approach to statistics but are new to the topics covered here, this book will provide an excellent starting place before you jump into the many references to the recent literature provided by the authors.
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4.0 out of 5 stars Recursive Partitioning, May 15 2000
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This review is from: Recursive Partitioning and Applications (Hardcover)
Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.
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