A compilation of the main results on Receiver Operating Characteristic (ROC) curve analysis, this book brings together information in a format readily understandable to anyone interested in analyzing ROC curves, regardless of their background. The authors deliberately keep the material general and avoid specific aspects of special applications areas. They describe the theory behind and the use of this important tool for assessing the performance of statistical and machine learning classification methods. The book pulls together early results with the results of recent research in a wide range of disciplines, including statistics, machine learning, and data mining.

About the Author

Wojtek J. Krzanowski is Emeritus Professor of Statistics at the University of Exeter and Senior Research Investigator at Imperial College. Dr. Krzanowski's research interests include multivariate analysis, statistical modeling, classification, and computational methods. He has published 6 books, over 30 book contributions, and 100 articles in scientific journals. David J. Hand is head of the statistics section and head of the mathematics in banking and finance program at Imperial College. Currently president of the Royal Statistical Society, Dr. Hand has been a recipient of the Guy Medal of the Royal Statistical Society, the Royal Society Wolfson Research Merit Award, and the IEEE ICDM Research Contributions Award. He has published extensively on a wide range of statistical topics.

Table of Contents

Introduction Background Classification Classifier performance assessment The ROC curve Population ROC Curves Introduction The ROC curve Slope of the ROC curve and optimality results Summary indices of the ROC curve The binormal model Estimation Introduction Preliminaries: classification rule and error rates Estimation of ROC curves Sampling properties and confidence intervals Estimating summary indices Further Inference on Single Curves Introduction Tests of separation of P and N population scores Sample size calculations Errors in measurements ROC Curves and Covariates Introduction Covariate adjustment of the ROC curve Covariate adjustment of summary statistics Incremental value Matching in case-control studies Comparing ROC Curves Introduction Comparing summary statistics of two ROC curves Comparing AUCs for two ROC curves Comparing entire curves Identifying where ROC curves differ Bayesian Methods Introduction General ROC analysis Meta-analysis Uncertain or unknown group labels Beyond the Basics Introduction Alternatives to ROC curves Convex hull ROC curves ROC curves for more than two classes Other issues Design and Interpretation Issues Introduction Missing values Bias in ROC studies Choice of optimum threshold Medical imaging Substantive Applications Introduction Machine learning Atmospheric sciences Geosciences Biosciences Finance Experimental psychology Sociology Appendix: ROC Software References Further reading suggestions appear at the end of each chapter.

Reviews

Drs. Krzanowski and Hand provide a thorough overview of ROC curve analysis, similar to books already available, but with a more comprehensive approach, including many recent advancements from the literature. ! Broad in scope, it covers not only application to medical testing but extends to other fields where ROC analysis can be very useful: geosciences, finance, psychology, and sociology. ! this book is an excellent enhancement to the biostatistics literature. It will be a helpful reference not only for those in medicine but for researchers in all sectors, including government, industry, and academia. It provides a very broad review of ROC curve analysis comprising recent developments and includes a very extensive reference list. --Journal of the American Statistical Association, Vol. 105, No. 492, December 2010 ! Wojtek Krzanowski and David Hand succeeded in writing the first comprehensive monograph on ROC curves for continuous data. Each chapter closes with references for further reading which keeps the book the size of a handy handbook containing an overview of the most important information on the topic while offering further references [for] the interested reader. The book is well structured and easy to read. ! highly recommended as [a] comprehensive handbook to researchers ! --ISCB News, No. 50, December 2010 ! there was a need in the literature for a book devoted solely to ROC curves ! This book aims to answer this need; it succeeds, by offering the reader a concise and informative treatment of ROC curves. Krzanowski and Hand's long research experience in multivariate analysis and classification is reflected in the book. They explain various aspects of ROC analysis very simply, using only the necessary mathematics. ! the authors expand the theory to more advanced concepts of statistical inference, using informal language accompanied by a large number of examples from various scientific areas. Hence, the reading is easy, interesting, and bound to stimulate curiosity for further exploration of the literature. ! very useful, highly readable, and can serve as a guide to ROC curves for any scientist who has the basic statistics background. I therefore recommend it to all researchers and practitioners who work with multivariate data, especially those who are concerned with classification problems. --Computing Reviews, November 2009