Statistical Modeling and Robust Inference for One-shot Devices

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 each chapter to help readers implement their own codes and use them in the proposed examples and in their own research on one-shot device-testing data. Researchers, mathematicians, engineers, and students working on accelerated-life 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 each chapter to help readers implement their own codes, use them in the proposed examples and in their own research on one-shot device-testing data