Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the perspective of health research applications. It includes detailed descriptions of the theoretical underpinnings of recently developed empirical likelihood-based methods. The emphasis throughout is on the application of the methods to the health sciences, with worked examples using real data.
- Provides a systematic overview of novel empirical likelihood techniques.
- Presents a good balance of theory, methods, and applications.
- Features detailed worked examples to illustrate the application of the methods.
- Includes R code for implementation.
The book material is attractive and easily understandable to scientists who are new to the research area and may attract statisticians interested in learning more about advanced nonparametric topics including various modern empirical likelihood methods. The book can be used by graduate students majoring in biostatistics, or in a related field, particularly for those who are interested in nonparametric methods with direct applications in Biomedicine.
Table of Contents
Basic Ingredients of the Empirical Likelihood.
EL Applying to Bayesian Paradigm.
EL for probability weighted moments.
Two group comparison and combining likelihoods for the incomplete data.
Empirical Likelihood for a U-Statistic Constraint.
EL Application to Receiver Operating Characteristic Curve analysis.
Albert Vexler obtained his Ph.D. degree in Statistics and Probability Theory from the Hebrew University of Jerusalem in 2003. Dr. Vexler was a postdoctoral research fellow in the Biometry and Mathematical Statistics Branch at the National Institutes of Health, USA. Dr. Vexler is a tenured Full Professor at the SUNY, Department of Biostatistics. Dr. Vexler has authored and co-authored various publications that contribute to both the theoretical and applied aspects of statistics.
Dr. Yu received her Ph.D. degree in Statistics from Texas A & M University in 2003. Her Ph.D. advisor was Thomas E. Wehrly, Ph.D. Currently, Dr. Yu is a tenured associate Professor at the State University of New York at Buffalo, Department of Biostatistics. Also, Dr. Yu is the director of the Population Health Observatory, School of Public Health and Health Professions at the State University of New York at Buffalo.
"As far as I know, Empirical Likelihood Methods in Biomedicine and Health is the first book that provides a compendium of nonparametric likelihood statistical techniques in the perspective of health research applications. It focuses on the application of the methods to health sciences, with worked examples using real data. … The book is well written. It is comprehensive and informative. The book material is attractive and easily understandable to scientists who are new to the research area and may attract statisticians interested in learning more about advanced nonparametric topics, including various modern empirical likelihood methods. The book contains interesting examples and plenty of R codes. These features make the book suitable as a self-learning text for applied statisticians. The book can also be used by graduate students majoring in biostatistics or in a related field, particularly for those who are interested in nonparametric methods with direct applications in biomedicine."
—Gengsheng Qin, Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, in Statistics in Medicine, January 2019
"This textbook discusses some of the classical statistical problems in the fields of biomedicine and health within the framework of empirical likelihood methodology as introduced and further explained by Owen (1988, 2001)... In an overall assessment, the book provides methodological and computational details for a wide range of problems found in biostatistics. This book is accessible to a wide audience (medical statisticians, researchers in public health, and others) and can serve as a textbook for a graduate special topics class ... The text is well structured. The methods are discussed from the point of view of both theory and applications. The authors have done a great job including R code to illustrate the theory. Furthermore, several data examples are used to motivate discussion of various modeling issues and to provide insight into the nature of the data that arise in this field. I greatly enjoyed reading the book, and I believe that it will serve as a valuable reference for anyone who wishes to study modern methods of inference in biomedicine and health."
- Konstantinos Fokianos, Lancaster University