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Statistical Modeling And Robust Inference For One-Shot Devices eBook

de Elena Castilla e Narayanaswamy Balakrishnan
Editor: ELSEVIER SCIENCE, março de 2025 ‧
182,85€
164,57€
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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

Statistical Modeling And Robust Inference For One-Shot Devices

de Elena Castilla e Narayanaswamy Balakrishnan

Propriedade Descrição
ISBN: 9780443141522
Editor: ELSEVIER SCIENCE
Data de Lançamento: março de 2025
Tipo de produto: eBook
Formato e Compatibilidade:
Classificação Temática: eBooks em Inglês > Ciências Exatas e Naturais > Matemática
EAN: 9780443141522
Acessibilidade: Ver características de acessibilidade indicadas pelo editor