Nozer D. Singpurwalla

eBook, PDF

Reliability and Risk (eBook, PDF)

A Bayesian Perspective

Beschreibung

We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications.

Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by:

• Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis.
• Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book.
• Introducing the notion of 'composite reliability', or the collective reliability of a population of items.
• Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk.

Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.

Technische Details

• Geräte: PC
• mit Kopierschutz
• Größe: 3.44MB

Barrierefreiheit

• Text-to-Speech

Autor

Nozer D. Singpurwalla is the author of Reliability and Risk: A Bayesian Perspective, published by Wiley.

Produktdetails

• Verlag: John Wiley & Sons
• Erscheinungstermin: 14. August 2006
• Englisch
• ISBN-13: 9780470060339
• Artikelnr.: 38190439

Rezensionen

"The book is written by an expert in reliability analysis and it isa very valuable source of information for mathematical models forreliability problems ... An extensive bibliography concludes thebook." (Stat Papers, 2011)

"As the author mentions in his preface, the book can be read inseveral different ways, as a text for a graduate level course onreliability or as a source book for "information and openproblems." This book has been a joy to read for this reviewer."(International Statistical Review, August 2008)

"Singpurwalla seems to be at his best in probabilistic modelingof reality. He has written what must be one of the first booksreliability written from a subjective, Bayesian point of view."(International Statistical

Review, August 2008)

"The material of this book will be most profitable forpractitioners and researchers in reliability and survivability, whowill greatly appreciate it as a source of information and openproblems." (Mathematical Reviews, 2008h)

"This is a very interesting, provocative, and worthwhile book."(Biometrics, June 2008)

"What I liked most about this book, however, is the way itblends interesting technical material with foundational discussionabout the nature of uncertainty." (Biometrics, June2008)

"The investigation of the theoretical models under considerationin the book is first class..." (Law, Probability and RiskAdvance Access, September 2007)

"I feel that I have learned an effective plotting technique fromthese plots..." (Technometrics, February 2008)

"...a cornucopia of probability models and inference methodsfor different problems...[that] serve as a rich taxonomy thatstatisticians can use to fit models...works as both aneducational tool and as a reference." (MAA Reviews, March 6,2007)