Heping Zhang – författare

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.

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.

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.

Multiple complex pathways, characterized by interrelated events and con­ ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments supporting many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an effective methodology for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-based constraints on the extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. How­ ever, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. Thus, the purpose of this book is to demon­ strate the effectiveness of a relatively recently developed methodology­ recursive partitioning-as a response to this challenge. We also compare and contrast what is learned via recursive partitioning with results ob­ tained 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 re­ gression techniques. This book is suitable for three broad groups of readers: (1) biomedical re­ searchers, 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 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.

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.

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.