This book is oriented equally towards reliability, involving data analysis from engineering and the physical sciences, and survival analysis, usually related to data analysis in the biomedical sciences. Features extensive exercises using SAS and SYSTAT.

This textbook is an introduction to lifetime data analysis aimed primarily at undergraduate and beginning graduate statistics students needing exposure to major topics of the subject, but not yet specializing in a particular application area such as engineering or the biomedical sciences. Unlike other textbooks at the same level, the work is oriented equally towards reliability and survival analysis. Featuring extensive exercises at all levels of difficulty employing well-known computing packages such as SAS and SYSTAT, the work presents real-world applications to the health sciences, manufacturing and industrial processes. The authors provide thorough explanations of methods and tests moving away from the "cookbook" approach commonly used in other textbooks at this level. A solutions manual will be available to instructors adopting the work as a course textbook.

The analysis of failure and lifetime data plays a major role in modern statistics. Typical examples include the analysis of the time and load at which a machine fails, as well as the analysis of the time it takes for a patient either to die or to reach a milestone in his or her illness or recovery. The former is an example of Reliabilty Analysis, common in engineering and physical sciences, while the latter is found in biomedical studies and falls under Survival Analysis category. This volume covers all areas pertaining to both Reliability Analysis and Survival Analysis. An Introduction to Lifetime Data Analysis may be used as a textbook for advanced undergraduate and graduate courses in statistics. Prerequisites include distribution theory and maximum likelihood based statistical inference, plus familiarity with standard elementary methods of analysis up to regression. The text is also an excellent reference for students, researchers, and practitioners in applied statistics.

Autorentext
N. BALAKRISHNAN, PhD, is Professor of Mathematics and Statistics at McMaster University in Hamilton, Ontario, Canada.V. B. NEVZOROV, PhD, DS, is Professor of Probability and Statistics at St. Petersburg State University in St. Petersburg, Russia.

Inhalt
Introduction.- Basic Concepts.- Models of Lifetime Data.- Examining Lifetime Data: Nonparametric Methods.- Fitting Parametric Lifetime Models.- Regression Models: Lifetime Data with Covariates.- Cox's Semiparametric Proportional Hazards Regression Model.- Recurrent Events and Repairable Systems.- System Reliability.- Bibliography.- Index