Statistical Methods for Reliability Data

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.

Autorentext

WILLIAM Q. MEEKER, PhD, is Professor of Statistics and
Distinguished Professor of Liberal Arts and Sciences at Iowa State
University. He is a Fellow of the American Statistical Association
and an elected member of the International Statistics Institute.
Among his many awards and honors are the Youdan Prize and two
Wilcoxon Prizes as well as two awards for outstanding teaching at
Iowa State. He is coauthor of Statistical Intervals: A Guide for
Practitioners (Wiley) and of numerous book chapters and
publications in the engineering and statistical literature. A
former editor of Technometrics and coeditor of Selected Tables in
Mathematical Statistics, he is currently Associate Editor for
International Statistical Review.

LUIS A. ESCOBAR, PhD, is a Professor in the Department of
Experimental Statistics at Louisiana State University. His research
and consulting interests include statistical analysis of
reliability data, accelerated testing, survival analysis, and
nonlinear models. An Associate Editor for Technometrics and the IIE
Transactions of Quality and Reliability Engineering, Professor
Escobar is a Fellow of the American Statistical Association and
elected member of the International Statistics Institute. He is the
author of several book chapters, and his publications have appeared
in the engineering and statistical literature.

Zusammenfassung
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.

Inhalt

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.

Epilogue.

Appendices.

References.

Indexes.