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This book improves Meeker and Escobar (1998, Wiley) not only in terms of organization and presentation, but also in extensions, modifications to the technical material, and advanced topic coverage (such as accelerated degradation and sensor, storage, and communications technology). It presents state-of-the-art, computer-based statistical methods for reliability data analysis, for test planning of industrial products, and for dynamic covariate information found on the Internet. It also improves long time established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. Bayesian methods in solving practical problems (e.g. models involving random effects or censoring that arises in reliability studies) are now incorporated where appropriate; the computations are done with WinBUGS. Ample exercises that extend and strengthen the concepts in the book are included. The criterion for integrating material in the book is that the authors have in-hand or have seen real applications for the methodology. The book is specifically geared for either a one-semester course on advanced topics in reliability theory in either a statistics or engineering department at the second-year graduate level or for researchers who need access to new and modern methodologies. R functions and subroutines, along with an extensive list of data sets, are included on a massive web site that is meticulously maintained by the authors.

Advanced Statistical Methods for Reliability Analysis