Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject.
One of the greatest chess legends of all time, Aron Nimzowitsch (1886-1935), is best known for founding the Hypermodernism school of chess, which emerged after World War I to challenge the chess ideologies of traditional central European masters. This first full-scale biography of Nimzowitsch chronicles his early life in Denmark, his family and education, and his fascination with the game that would become the focus of his life. Also included are explorations of his tournament games and records, his dispute with influential chess teacher Siegbert Tarrasch, and his role in the development of Hypermodern Chess. With detailed accounts of nearly 450 games and the only narrative of Nimzowitsch from 1914 to 1924, a period formerly cloaked in mystery, this volume offers the most thorough profile available of one of chess's greatest innovators.
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health. The book includes mathematical detail which can be skipped by readers more interested in the practical examples. It is aimed at biostatisticians and at readers with an interest in the topic having a more applied background, such as epidemiology. This book builds on several courses the authors have taught on the subject. Key Features: · Intensity-based and marginal models. · Survival data, competing risks, illness-death models, recurrent events. · Includes a full chapter on pseudo-values. · Intuitive introductions and mathematical details. · Practical examples of event history data. · Exercises. Software code in R and SAS and the data used in the book can be found on the book’s webpage.
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