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Titlebook: Springer Handbook of Engineering Statistics; Hoang Pham Book 2023Latest edition Springer-Verlag London Ltd., part of Springer Nature 2023

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發(fā)表于 2025-3-21 17:37:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Springer Handbook of Engineering Statistics
編輯Hoang Pham
視頻videohttp://file.papertrans.cn/875/874971/874971.mp4
概述New edition chapters on hot topics including big data, statistical AI, and statistical reliability modeling.A wide selection of statistical techniques and tools enable the reader to choose the most ap
叢書名稱Springer Handbooks
圖書封面Titlebook: Springer Handbook of Engineering Statistics; Hoang Pham Book 2023Latest edition Springer-Verlag London Ltd., part of Springer Nature 2023
描述.This handbook.?.gathers the full range of statistical techniques and tools required by engineers, analysts and scientists from all fields.?The book is a comprehensive place to look for methods and solutions to practical problems within - but not limited to - data science, quality assurance in design and production engineering.??.The tools of engineering statistics are relevant for modeling and prediction of products, processes and services, but also for the analysis of ongoing processes, the reliability and life-cycle assessment of products and services, and finally to achieve realistic predictions on how to improve processes and products.? ..This book contains contributions from around 115 leading experts in statistics, biostatistics, engineering statistics, reliability engineering, and related areas. It covers the various methods as well as their applications from industrial control to failure mechanism and analysis, medicine, businessintelligence, electronic packaging, and risk management. It enables readers to choose the most appropriate method through its wide range of selection of statistical techniques and tools. ..For the second edition all chapters have been thoroughly up
出版日期Book 2023Latest edition
關(guān)鍵詞Engineering Statistics; Applied Statistics and Engineering; Engineering and Business Management; Data S
版次2
doihttps://doi.org/10.1007/978-1-4471-7503-2
isbn_ebook978-1-4471-7503-2Series ISSN 2522-8692 Series E-ISSN 2522-8706
issn_series 2522-8692
copyrightSpringer-Verlag London Ltd., part of Springer Nature 2023
The information of publication is updating

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Emilio Ferrari,Arrigo Pareschi,Alberto Regattieri,Alessandro Persona
板凳
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Nam-Ky Nguyen,Ron S. Kenett,Tung-Dinh Pham,Mai Phuong Vuong
地板
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Statistics for Reliability Modelingd. Special regression techniques for degradation data can be used to draw inference on the underlying lifetime distribution, even if failures are rarely observed..The last part of the chapter discusses reliability for systems. Along with the components that comprise the system, reliability analysis
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Symmetric Geometric Skew Normal Regression Modelown parameters cannot be obtained in explicit forms. In this paper, we have proposed a very efficient expectation maximization (EM) algorithm, and it is observed that the proposed EM algorithm works very well. We have further considered a location shift SGSN regression model. It is a more flexible t
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發(fā)表于 2025-3-22 21:55:09 | 只看該作者
Mathematical Reliability Aspects of Multivariate Probability Distributionstern Illinois University, Chicago, 4 May 2017). In applications, such as system reliability modeling and other, among all the available .-variate solutions, one needs to choose those that may fit particular data, and, after that, test the chosen models by proper statistical methods. The theoretical
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