β Scribed by William Q. Meeker, Luis A. Escobar
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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 conceptsAn 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.
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This is the most complete reliability book that I have seen. It is appropriate as both a textbook and a reference. It is well-written and easy to understand. I highly recommend this book for anybody interested in learning reliability theory.
Spatial statistics-analyzing spatial data through statistical models-has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific lite
Third Edition brings the text up to date with new material and updated references.New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional
Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis prob
Praise for the Second Edition"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."βTechnometricsThis new edition continues to provide the most current, proven statistical methods for quality control and quality improvementThe use of
<p><span>Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource</span></p><p><span>Statistical Learning with Big Dependent Data</span><span> delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzi