Statistical Modeling and Robust Interference for One-shot Devices offers a comprehensive investigation on robust techniques for one-shot devices under accelerated life tests. With numerous examples, case studies, and included R codes in each chapter, this book helps readers implement their own codes, use them in proposed examples, and conduct 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 surely find this to be a welcomed resource. The study of one-shot devices such as automobile airbags, fire extinguishers, and antigen tests is rapidly becoming an important problem in the area of reliability engineering. These devices, which get destroyed or must be rebuilt after use, are particular cases of extreme censoring, which makes the problem of estimating their reliability and lifetime challenging. As classical statistical and inferential methods do not consider the issue of robustness, this book is a welcomed addition to the conversation.
Table of Contents
1. Introduction 2. Preliminaries 3. Divergence Measures and their Application to One-shot Device Testing 4. Robust Inference under the Exponential Distribution 5. Robust Inference under the Gamma Distribution 6. Robust Inference under the Weibull Distribution 7. Robust Inference under the Lognormal distribution 8. Robust Inference under the Proportional Hazards Model 9. Robust Inference under the Exponential Distribution and Competing Risks 10. Robust Inference under the Weibull Distribution and Competing Risks 11. Robust Optimal Design of Accelerated Life Tests for One-Shot Device Testing 12. Conclusions and Future Directions
