This volume dedicated to William Q. Meeker on the occasion of his sixtieth birthday is a collection of invited chapters covering recent advances in accelerated life testing and degradation models. The book covers a wide range of applications to areas such as reliability, quality control, the health sciences, economics and finance. Additional topics covered include accelerated testing and inference, step-stress testing and inference, nonparametric inference, model validity in accelerated testing, the point process approach, bootstrap methods in degradation analysis, exact inferential methods in reliability, dynamic perturbed systems, and degradation models in statistics. Advances in Degradation Modeling is an excellent reference for researchers and practitioners in applied probability and statistics, industrial statistics, the health sciences, quality control, economics, and finance.

Probability, Statistics and Modelling in Public Health consists of refereed contributions by expert biostatisticians that discuss various probabilistic and statistical models used in public health. Many of them are based on the work of Marvin Zelen of the Harvard School of Public Health. Topics discussed include models based on Markov and semi-Markov processes, multi-state models, models and methods in lifetime data analysis, accelerated failure models, design and analysis of clinical trials, Bayesian methods, pharmaceutical and environmental statistics, degradation models, epidemiological methods, screening programs, early detection of diseases, and measurement and analysis of quality of life.

This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics.

The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.

Probability, Statistics and Modelling in Public Health consists of refereed contributions by expert biostatisticians that discuss various probabilistic and statistical models used in public health. Many of them are based on the work of Marvin Zelen of the Harvard School of Public Health. Topics discussed include models based on Markov and semi-Markov processes, multi-state models, models and methods in lifetime data analysis, accelerated failure models, design and analysis of clinical trials, Bayesian methods, pharmaceutical and environmental statistics, degradation models, epidemiological methods, screening programs, early detection of diseases, and measurement and analysis of quality of life.