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.
Table of Contents
1. Introduction 2. Inference for One-Shot Devices with a Single Failure mode 3. Divergence Measures and their Application to One-Shot Devices with a Single Failure mode 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. Inference for One-Shot Devices with Multiple Failure Modes 10. Robust Inference under the Exponential Distribution and Competing Risks 11. Robust Inference under the Weibull Distribution and Competing Risks 12. Robust Inference under Cyclic Accelerated Life Tests 13. Summary and Future Directions Appendix A Derivation of the Influence Function of the Weighted Minimum DPD Estimators
