Statistical Modeling and Robust Inference for One-shot Devices
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Form:Einzelkauf Download
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Sprache:Englisch
160,00 €
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Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
08.04.2025
Verlag
Elsevier Science & Techn.Seitenzahl
250 (Printausgabe)
Dateigröße
29310 KB
Sprache
Englisch
EAN
9780443141522
The study of one-shot devices such as automobile airbags, fire extinguishers, or antigen tests, is rapidly becoming an important problem in the area of reliability engineering. These devices, which are destroyed or must be rebuilt after use, are a particular case of extreme censoring, which makes the problem of estimating their reliability and lifetime challenging. However, classical statistical and inferential methods do not consider the issue of robustness.Statistical Modeling and Robust Interference for One-shot Devices offers a comprehensive investigation of robust techniques of one-shot devices under accelerated-life tests. With numerous examples and case studies in which the proposed methods are applied, this book includes detailed R codes in selected chapters to help readers implement their own codes and use them in the proposed examples and in their own research on one-shot devicetesting data. Researchers, mathematicians, engineers, and students working on acceleratedlife testing data analysis and robust methodologies will find this to be a welcome resource.
- Offers an indepth review of statistical methods for the testing and analysis of one-shot devices
- Includes numerous examples and case studies in which the proposed methods are applied
- Introduces detailed R codes in selected chapters to help readers implement their own codes, use them in the proposed examples and in their own research on one-shot device-testing data
- Offers an indepth review of statistical methods for the testing and analysis of one-shot devices
- Includes numerous examples and case studies in which the proposed methods are applied
- Introduces detailed R codes in selected chapters to help readers implement their own codes, use them in the proposed examples and in their own research on one-shot device-testing data
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