Statistical Models Based on Counting Processes (Springer Series in Statistics)

Statistical Models Based on Counting Processes (Springer Series in Statistics)

by RichardD.Gill (Author), NielsKeiding (Author), OrnulfBorgan (Author), PerK.Andersen (Author)

Synopsis

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.

$156.29

Quantity

10 in stock

More Information

Format: Paperback
Pages: 795
Edition: 1st ed. 1993. Corr. 4th printing
Publisher: Springer
Published: 01 Jan 1997

ISBN 10: 0387945199
ISBN 13: 9780387945194
Book Overview: Springer Book Archives

Media Reviews

This book is a masterful account of the counting process approach...is certain to be the standard reference for the area, and should be on the bookshelf of anyone interested in event-history analysis. International Statistical Institute Short Book Reviews

...this impressive reference, which contains a a wealth of powerful mathematics, practical examples, and analytic insights, as well as a complete integration of historical developments and recent advances in event history analysis. Journal of the American Statistical Association