Recursive partitioning in the health sciences信息编码、析取与分配的数学（丛书）
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作者： Heping Zhang等著
出 版 社：
出版时间： 1999-3-1字数：版次： 1页数： 226印刷时间： 1999/03/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780387986715包装： 精装编辑推荐
作者简介：
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.
内容简介
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.
目录
Preface
1Introduction
1.1 Examples Using CART
1.2 The Statistical Problem
1.3 Outline of the Methodology
2 A Practical Guide to Tree Construction
2.1 The Elements of Tree Construction
2.2 Splitting a Node
2.3 Terminal Nodes
2.4 Download and Use of Software
3Logistic Regression
3.1 Logistic Regression Models
3.2 A Logistic Regression Analysis
4Classification Trees for a Binary Response
4.1 Node Impurity
4.2 Determination of Terminal Nodes
4.2.1 Misclassification Cost
4.2.2 Cost Complexity
4.2.3 Nested Optimal Subtrees
4.3 The Standard Error of Rcu
4.4 Tree-Based Analysis of the Yale Pregnancy Outcome Study
4.5 An Alternative Pruning Approach
4.6 Localized Cross-Validation
4.7 Comparison Between Tree-Based and Logistic Regression Analyses
4.8 Missing Data
4.8.1 Missings Together Approach
4.8.2 Surrogate Splits
4.9 Tree Stability
4.10 Implementation
5Risk-Factor Analysis Using Tree-Based Stratification
5.1 Background
5.2 The Analysis
6Analysis of Censored Data: Examples
6.1 Introduction
6.2 Tree-Based Analysis for the Western Collaborative Group Study Data
7Analysis of Censored Data:Concepts and Classical Methods
7.1 The Basics of Survival Analysis
7.1.1 Kaplan-Meier Curve
7.1.2 Log-Rank Test
7.2 Parametric Regression for Censored Data
7.2.1 Linear Regression with Censored Data
7.2.2 Cox Proportional Hazard Regression
7.2.3 Reanalysis of the Western Collaborative Group Study Data
8Analysis of Censored Data: Survival Trees
8.1 Splitting Criteria
8.1.1 Gordon and Olshen's Rule
8.1.2 Maximizing the Difference
8.1.3 Use of Likelihood Functions
8.1.4 A Straightforward Extension
8.2 Pruning a Survival Tree
8.3 Implementation
8.4 Survival Trees for the Western Collaborative Group Study Data
9 Regression Trees adn Adaptive Splines for a Continuous Response
10 Analysis of Longitudinal Data
11 Analysis of Multiple Discrete Responses
12 Appendix
References
Index
