Advanced Statistical Methods For Reliability Analysis

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Advanced Statistical Methods For Reliability Analysis


Advanced Statistical Methods For Reliability Analysis
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Advanced Statistical Methods For Reliability Analysis


Advanced Statistical Methods For Reliability Analysis
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Author : William Q. Meeker
language : en
Publisher: Wiley-Blackwell
Release Date : 2021-04-05

Advanced Statistical Methods For Reliability Analysis written by William Q. Meeker and has been published by Wiley-Blackwell this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-05 with categories.


This book improves Meeker and Escobar (1998, Wiley) not only in terms of organization and presentation, but also in extensions, modifications to the technical material, and advanced topic coverage (such as accelerated degradation and sensor, storage, and communications technology). It presents state-of-the-art, computer-based statistical methods for reliability data analysis, for test planning of industrial products, and for dynamic covariate information found on the Internet. It also improves long time 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. Bayesian methods in solving practical problems (e.g. models involving random effects or censoring that arises in reliability studies) are now incorporated where appropriate; the computations are done with WinBUGS. Ample exercises that extend and strengthen the concepts in the book are included. The criterion for integrating material in the book is that the authors have in-hand or have seen real applications for the methodology. The book is specifically geared for either a one-semester course on advanced topics in reliability theory in either a statistics or engineering department at the second-year graduate level or for researchers who need access to new and modern methodologies. R functions and subroutines, along with an extensive list of data sets, are included on a massive web site that is meticulously maintained by the authors.

Statistical Reliability Engineering


Statistical Reliability Engineering
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Author : Boris Gnedenko
language : en
Publisher: John Wiley & Sons
Release Date : 1999-05-03

Statistical Reliability Engineering written by Boris Gnedenko and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-05-03 with Technology & Engineering categories.


Proven statistical reliability analysis methods-available for the first time to engineers in the West While probabilistic methods of system reliability analysis have reached an unparalleled degree of refinement, Russian engineers have concentrated on developing more advanced statistical methods. Over the past several decades, their efforts have yielded highly evolved statistical models that have proven to be especially valuable in the estimation of reliability based upon tests of individual units of systems. Now Statistical Reliability Engineering affords engineers a unique opportunity to learn both the theory behind and applications of those statistical methods. Written by three leading innovators in the field, Statistical Reliability Engineering: * Covers all mathematical models for statistical reliability analysis, including Bayesian estimation, accelerated testing, and Monte Carlo simulation * Focuses on the estimation of various measures of system reliability based on the testing of individual units * Contains new theoretical results available for the first time in print * Features numerous examples demonstrating practical applications of the theory presented Statistical Reliability Engineering is an important professional resource for reliability and design engineers, especially those in the telecommunications and electronics industries. It is also an excellent course text for advanced courses in reliability engineering.

Statistical Methods For Reliability Data


Statistical Methods For Reliability Data
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Author : William Q. Meeker
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-24

Statistical Methods For Reliability Data written by William Q. Meeker and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-24 with Technology & Engineering categories.


An authoritative guide to the most recent advances in statistical methods for quantifying reliability Statistical Methods for Reliability Data, Second Edition (SMRD2) is an essential guide to the most widely used and recently developed statistical methods for reliability data analysis and reliability test planning. Written by three experts in the area, SMRD2 updates and extends the long- established statistical techniques and shows how to apply powerful graphical, numerical, and simulation-based methods to a range of applications in reliability. SMRD2 is a comprehensive resource that describes maximum likelihood and Bayesian methods for solving practical problems that arise in product reliability and similar areas of application. SMRD2 illustrates methods with numerous applications and all the data sets are available on the book’s website. Also, SMRD2 contains an extensive collection of exercises that will enhance its use as a course textbook. The SMRD2's website contains valuable resources, including R packages, Stan model codes, presentation slides, technical notes, information about commercial software for reliability data analysis, and csv files for the 93 data sets used in the book's examples and exercises. The importance of statistical methods in the area of engineering reliability continues to grow and SMRD2 offers an updated guide for, exploring, modeling, and drawing conclusions from reliability data. SMRD2 features: Contains a wealth of information on modern methods and techniques for reliability data analysis Offers discussions on the practical problem-solving power of various Bayesian inference methods Provides examples of Bayesian data analysis performed using the R interface to the Stan system based on Stan models that are available on the book's website Includes helpful technical-problem and data-analysis exercise sets at the end of every chapter Presents illustrative computer graphics that highlight data, results of analyses, and technical concepts Written for engineers and statisticians in industry and academia, Statistical Methods for Reliability Data, Second Edition offers an authoritative guide to this important topic.

Quantile Based Reliability Analysis


Quantile Based Reliability Analysis
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Author : N. Unnikrishnan Nair
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-24

Quantile Based Reliability Analysis written by N. Unnikrishnan Nair and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-24 with Mathematics categories.


This book provides a fresh approach to reliability theory, an area that has gained increasing relevance in fields from statistics and engineering to demography and insurance. Its innovative use of quantile functions gives an analysis of lifetime data that is generally simpler, more robust, and more accurate than the traditional methods, and opens the door for further research in a wide variety of fields involving statistical analysis. In addition, the book can be used to good effect in the classroom as a text for advanced undergraduate and graduate courses in Reliability and Statistics.

Reliability Modelling


Reliability Modelling
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Author : Linda C. Wolstenholme
language : en
Publisher: Routledge
Release Date : 2018-10-03

Reliability Modelling written by Linda C. Wolstenholme and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Business & Economics categories.


Reliability is an essential concept in mathematics, computing, research, and all disciplines of engineering, and reliability as a characteristic is, in fact, a probability. Therefore, in this book, the author uses the statistical approach to reliability modelling along with the MINITAB software package to provide a comprehensive treatment of modelling, from the basics through advanced modelling techniques.The book begins by presenting a thorough grounding in the elements of modelling the lifetime of a single, non-repairable unit. Assuming no prior knowledge of the subject, the author includes a guide to all the fundamentals of probability theory, defines the various measures associated with reliability, then describes and discusses the more common lifetime models: the exponential, Weibull, normal, lognormal and gamma distributions. She concludes the groundwork by looking at ways of choosing and fitting the most appropriate model to a given data set, paying particular attention to two critical points: the effect of censored data and estimating lifetimes in the tail of the distribution.The focus then shifts to topics somewhat more difficult:the difference in the analysis of lifetimes for repairable versus non-repairable systems and whether repair truly ""renews"" the systemmethods for dealing with system with reliability characteristic specified for more than one component or subsystemthe effect of different types of maintenance strategiesthe analysis of life test dataThe final chapter provides snapshot introductions to a range of advanced models and presents two case studies that illustrate various ideas from throughout the book.

Statistics


Statistics
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Author : Mirabelle Harper
language : en
Publisher:
Release Date : 2021-04-27

Statistics written by Mirabelle Harper and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with categories.


Mirabelle Harper's Statistics Analysis And Presentation Of Data - Amstat News asked three editors to rate their top five favorite books in the September 2003 issue. Statistical methods for reliability data were chosen. Provide statistical methods to test the reliability according to the age of the computer This volume presents advanced computer statistical methods to analyze reliability data and design tests for industrial products. Statistical Reliability Methods The data updates and improves established techniques as it demonstrates how new graphical, numerical, or simulation-based methods can be applied to a wide range of models experienced in the analysis of reliability data. This includes methods for designing reliability studies and analyzing degradation data, simulation methods used to supplement large-sample asymptotic theory, general probability-based methods for dealing with arbitrarily censored and truncated data, And much more. In this book, industrial and academic engineers and statisticians find: There is a wealth of information and developed procedures that provide a competitive advantage to products. Simple examples of analysis of data calculated with the S-PLUS system, for which functions and commands are available via the Internet End of chapter, practical sets of real data Many infographics illustrate hundreds of data, results of analyzes and technical concepts. It is an essential resource for professionals working on product reliability and design decisions. Statistical Methods for Reliability Data is also an excellent textbook for workplace and university courses in applied reliability data analysis at the graduate level. An instructor's manual with detailed solutions to all of the book's problems is available on request from Wiley Editorial.

Understanding Advanced Statistical Methods


Understanding Advanced Statistical Methods
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Author : Peter Westfall
language : en
Publisher: CRC Press
Release Date : 2013-04-09

Understanding Advanced Statistical Methods written by Peter Westfall and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-09 with Mathematics categories.


Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book’s website.