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Statistical Methods for Reliability Data

William Q. Meeker, Luis A. Escobar

ISBN: 978-0-471-14328-4 August 1998 712 Pages


Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen.

Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products. Statistical Methods for Reliability Data updates and improves 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. It includes methods for planning reliability studies and analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, general likelihood-based methods of handling arbitrarily censored data and truncated data, and more. In this book, engineers and statisticians in industry and academia will find:

  • A wealth of information and procedures developed to give products a competitive edge
  • Simple examples of data analysis computed with the S-PLUS system-for which a suite of functions and commands is available over the Internet
  • End-of-chapter, real-data exercise sets
  • Hundreds of computer graphics illustrating data, results of analyses, and technical concepts

An essential resource for practitioners involved in product reliability and design decisions, Statistical Methods for Reliability Data is also an excellent textbook for on-the-job training courses, and for university courses on applied reliability data analysis at the graduate level.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon requestfrom the Wiley editorial department.

Related Resources

Partial table of contents:

Reliability Concepts and Reliability Data.

Nonparametric Estimation.

Other Parametric Distributions.

Probability Plotting.

Bootstrap Confidence Intervals.

Planning Life Tests.

Degradation Data, Models, and Data Analysis.

Introduction to the Use of Bayesian Methods for Reliability Data.

Failure-Time Regression Analysis.

Accelerated Test Models.

Accelerated Life Tests.

Case Studies and Further Applications.




"…provides state-of-the-art developments in reliability theory and applications." (Journal of Statistical Computation and Simulation, June 2005)