Applied Life Data Analysis (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

About the author: Wayne Nelson is a consulting statistician with the General Electric Company. He also consults on and teaches engineering and scientific applications of statistical methods for other companies throughout the country. In 1981 he received General Electric?s Dushman Award in recognition of his outstanding contributions to research and applications in product life data analysis and accelerated testing. Dr. Nelson is an Adjunct Professor at Union College and Rensselaer Polytechnic Institute, where he teaches graduate courses on the theory and application of statistics. A widely published author on statistical methods for engineering problems, he is also a Fellow of the American Statistical Association, and a member of the American Society for Quality Control, the Institute of Electrical and Electronics Engineers, and the Biometrics Society. Dr. Nelson received his Ph.D. in statistics from the University of Illinois in 1965.

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WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"Many examples drawn from the author’s experience of engineering applications are used to illustrate the theoretical results, which are presented in a cookbook fashion...it provides an excellent practical guide to the analysis of product-life data."
–T.M.M. Farley
Special Programme of Research in Human Reproduction
World Health Organization
Geneva, Switzerland
Review in Biometrics, September 1983

Now a classic, Applied Life Data Analysis has been widely used by thousands of engineers and industrial statisticians to obtain information from life data on consumer, industrial, and military products. Organized to serve practitioners, this book starts with basic models and simple informative probability plots of life data. Then it progresses through advanced analytical methods, including maximum likelihood fitting of advanced models to life data. All data analysis methods are illustrated with numerous clients' applications from the author's consulting experience.

Aus dem Klappentext

This practical, highly readable work presents the latest methods in life data analysis ? both simple and sophisticated ones for predicting the time-to-failure of a product or living organism. Life data analysis is currently used to predict warranty costs, evaluate replacement policies, assess design changes, and compare alternate designs, vendors, materials, and manufacturing methods. It is also a valuable tool in the comparison of alternative medical treatments. In both organization and scope Applied Life Data Analysis is designed to serve the day-to-day user of statistics ? industrial statisticians, engineers, and other practitioners in fields where survival data are studied, such as medicine, biology, actuarial science, economics, business, and criminology. Its logical, step-by-step development of the methodology progresses from basic models and simple graphical analyses of data through advanced analytical methods, with each topic self-contained for easy reference. A wealth of practical numerical examples using real data simplify the application of the techniques. Derivations are omitted unless they prove helpful in understanding the material. Among the useful tools explained are:

• simple graphical methods for estimating a life distribution from complete and censored life data
• statistical models and analyses for data on competing failure modes and on series systems
• linear and maximum likelihood methods for estimating life distributions from complete and censored data
• methods for analyzing inspection data (quantal-response and interval data)
• and methods for comparing samples (hypothesis tests) and for pooling estimates from a number of samples.