Heping Zhang

Burton H. Singer

A demonstration of the recursive partitioning methodology and its effectiveness as a response to the challenge of analysing and interpreting multiple complex pathways to many illnesses, diseases, and ultimately death. For comparison purposes, standard regression methods are presented briefly and then applied in the examples. This book is suitable for three broad groups of readers: biomedical researchers, clinicians, public health practitioners including epidemiologists, health service researchers, and environmental policy advisers; consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients'problems; and statisticians interested in methodological and theoretical issues. The book provides an up-to-date summary of the methodological and theoretical underpinnings of recursive partitioning, as well as a host of unsolved problems the solutions of which would advance the rigorous underpinnings of statistics in general.

Recursive Partitioning in the Health Sciences

Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology—recursive partitioning—as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where—and for what kinds of questions—recursive partitioning–based strategies have a decisive advantage over classical regression techniques.

Recursive Partitioning and Applications

Yaoting Zhang

Jian Huang

This book encompasses a wide range of important topics. The articles cover the following areas: asymptotic theory and inference, biostatistics, economics and finance, statistical computing and Bayesian statistics, and statistical genetics. Specifically, the issues that are studied include large deviation, deviation inequalities, local sensitivity of model misspecification in likelihood inference, empirical likelihood confidence intervals, uniform convergence rates in density estimation, randomized designs in clinical trials, MCMC and EM algorithms, approximation of p-values in multipoint linkage analysis, use of mixture models in genetic studies, and design and analysis of quantitative traits.

In Celebration of Prof. Yaoting Zhang's 70th Birthday

Development of Modern Statistics and Related Topics

Yimin Cai

The book summarizes the latest research and developments in dairy biotechnology and engineering. It provides a strategic approach for readers relating to fundamental research and practical work with lactic acid bacteria. The book covers every aspect from identification, ecology, taxonomy and industrial use. All contributors are experts who have substantial experience in the corresponding research field. The book is intended for researchers in the human, animal, and food sciences related to lactic acid bacteria. Dr. Heping Zhang is a Professor at the Key Laboratory of Dairy Biotechnology and Engineering Ministry of Education, Inner Mongolia Agricultural University, China. Dr. Yimin Cai works in Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Japan.

Heping Zhang

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