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Recursive Partitioning and Applications

  • Heping Zhang
  • Burton H. Singer

Part of the Springer Series in Statistics book series (SSS, volume 0)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Heping Zhang, Burton H. Singer
    Pages 1-8
  3. Heping Zhang, Burton H. Singer
    Pages 9-22
  4. Heping Zhang, Burton H. Singer
    Pages 23-29
  5. Heping Zhang, Burton H. Singer
    Pages 31-62
  6. Heping Zhang, Burton H. Singer
    Pages 63-77
  7. Heping Zhang, Burton H. Singer
    Pages 79-95
  8. Heping Zhang, Burton H. Singer
    Pages 97-103
  9. Heping Zhang, Burton H. Singer
    Pages 105-118
  10. Heping Zhang, Burton H. Singer
    Pages 119-131
  11. Heping Zhang, Burton H. Singer
    Pages 133-162
  12. Heping Zhang, Burton H. Singer
    Pages 163-198
  13. Heping Zhang, Burton H. Singer
    Pages 199-225
  14. Heping Zhang, Burton H. Singer
    Pages 227-235
  15. Back Matter
    Pages 237-259

About this book

Introduction

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. Heping Zhang is Professor of Public Health, Statistics, and Child Study, and director of the Collaborative Center for Statistics in Science, at Yale University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, a Myrto Lefkopoulou Distinguished Lecturer Awarded by Harvard School of Public Health, and a Medallion lecturer selected by the Institute of Mathematical Statistics. Burton Singer is Courtesy Professor in the Emerging Pathogens Institute at University of Florida, and previously Charles and Marie Robertson Professor of Public and International Affairs at Princeton University. He is a member of the National Academy of Sciences and Institute of Medicine of the National Academies, and a Fellow of the American Statistical Association.

Keywords

Logistic Regression Practical Computational Methods Recursive Partitioning Tree-based Survival Analysis Trees and Associated Forests adIOMEDICIaptive Splines and Regression Trees calculus classification epidemiology

Authors and affiliations

  • Heping Zhang
    • 1
  • Burton H. Singer
    • 2
  1. 1.Dept. BiostatisticsYale School of Public HealthNew HavenUSA
  2. 2.Emerging Pathogens InstituteUniversity of FloridaGainesvilleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-6824-1
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-6823-4
  • Online ISBN 978-1-4419-6824-1
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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