Statistical Modeling and Robust Inference for One-shot Devices
- 1st Edition - March 26, 2025
- Imprint: Academic Press
- Authors: Narayanaswamy Balakrishnan, Elena Castilla
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 4 1 5 3 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 4 1 5 2 - 2
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. Th… Read more
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Request a sales quoteThe 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
Graduate students, researchers, and professional mathematicians, statisticians, and engineers working on accelerated life testing data analysis and robust methodologies
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of figures
- List of tables
- Biography
- Preface
- Chapter 1: Introduction
- 1.1. Brief overview
- 1.2. Literature survey on one-shot devices
- 1.3. Scope and content of the book
- 1.4. Some real-life examples
- 1.4.1. Electro-explosive devices
- 1.4.2. Electric current
- 1.4.3. Solder joints
- 1.4.4. Osteoporosis in old people
- 1.4.5. Glass capacitors
- 1.4.6. Murine model of pneumonic melioidosis
- 1.4.7. Benzidine dihydrochloride (BDC) experiment
- 1.4.8. ED01 experiment
- 1.4.9. Class-B motor insulation
- 1.4.10. CSP solder joints
- Chapter 2: Inference for one-shot devices with a single failure mode
- 2.1. Brief overview
- 2.2. Model formulation and likelihood inference
- 2.2.1. ML estimation
- 2.2.2. Confidence intervals
- 2.2.3. Wald tests
- 2.3. Model misspecification
- 2.4. Bayesian inference
- 2.5. Non-parametric inference
- Chapter 3: Divergence measures and their application to one-shot devices with a single failure mode
- 3.1. Brief overview
- 3.2. Motivation of robust inference for one-shot devices
- 3.3. Weighted minimum DPD estimators
- 3.4. Divergence-based confidence intervals
- 3.5. Wald-type tests
- 3.5.1. Power function of the Wald-type tests
- 3.6. Influence functions
- 3.6.1. Influence function of weighted minimum DPD estimators
- 3.6.2. Influence function of Wald-type tests
- 3.7. On the choice of the optimal tuning parameter
- 3.7.1. Warwick and Jones' algorithm
- 3.7.2. Minimization of discrepancy distances
- Chapter 4: Robust inference under the exponential distribution
- 4.1. Brief overview
- 4.2. Model formulation
- 4.3. Weighted minimum DPD estimators
- 4.4. Influence functions
- 4.5. Simulation studies
- 4.6. Case studies
- Chapter 5: Robust inference under the gamma distribution
- 5.1. Brief overview
- 5.2. Model formulation
- 5.3. Weighted minimum DPD estimators
- 5.4. Influence functions
- 5.5. Simulation studies
- 5.6. Case studies
- Chapter 6: Robust inference under the Weibull distribution
- 6.1. Brief overview
- 6.2. Model formulation
- 6.3. Weighted minimum DPD estimators
- 6.4. Influence functions
- 6.5. Simulation studies
- 6.6. Case studies
- Chapter 7: Robust inference under the lognormal distribution
- 7.1. Brief overview
- 7.2. Model formulation
- 7.3. Weighted minimum DPD estimators
- 7.4. Influence functions
- 7.5. Simulation studies
- 7.6. Case studies
- Chapter 8: Robust inference under the proportional hazards model
- 8.1. Brief overview
- 8.2. Model formulation
- 8.3. Weighted minimum DPD estimators
- 8.4. Influence functions
- 8.5. Simulation studies
- 8.6. Case studies
- Chapter 9: Inference for one-shot devices with multiple failure modes
- 9.1. Brief overview
- 9.2. Competing risks models for one-shot devices
- 9.2.1. Without masked failure modes
- 9.2.2. With masked failure modes
- 9.3. Models for dependent failure modes for one-shot devices
- 9.4. Weighted minimum DPD estimators for competing risk model
- 9.4.1. Wald-type tests
- 9.4.2. On the choice of the optimal tuning parameter
- Chapter 10: Robust inference under the exponential distribution and competing risks
- 10.1. Brief overview
- 10.2. Model formulation
- 10.3. Weighted minimum DPD estimators
- 10.4. Simulation studies
- 10.5. Case studies
- Chapter 11: Robust inference under the Weibull distribution and competing risks
- 11.1. Brief overview
- 11.2. Model formulation
- 11.3. Weighted minimum DPD estimators
- 11.4. Simulation studies
- 11.5. Case studies
- Chapter 12: Robust inference under cyclic accelerated life tests
- 12.1. Brief overview
- 12.2. Cyclic models
- 12.3. Model formulation and likelihood inference
- 12.3.1. ML estimation
- 12.3.2. Finding suitable initial values
- 12.3.3. Asymptotic distribution and ML-based confidence intervals
- 12.4. Weighted minimum DPD estimators
- 12.5. Model selection
- 12.6. Simulation studies
- 12.6.1. Weighted minimum DPD estimators and confidence intervals
- 12.6.2. Model selection criterion
- 12.7. Case studies
- Chapter 13: Summary and future directions
- 13.1. Summary
- 13.2. Future directions
- 13.2.1. Robust inference for one-shot devices with correlated failure modes using copula models
- 13.2.2. Robust optimal design of ALTs for one-shot device testing
- 13.2.3. Model validation and goodness-of-fit methods
- 13.2.4. Non-proportional hazards model
- Appendix A: Derivation of the influence function of the weighted minimum DPD estimators
- References
- Index
- Edition: 1
- Published: March 26, 2025
- Imprint: Academic Press
- No. of pages: 250
- Language: English
- Paperback ISBN: 9780443141539
- eBook ISBN: 9780443141522
NB
Narayanaswamy Balakrishnan
Narayanaswamy Balakrishnan is a distinguished university professor in the Department of Mathematics and Statistics at McMaster University Hamilton, Ontario, Canada. He is an internationally recognized expert on statistical distribution theory, and a book-powerhouse with over 24 authored books, four authored handbooks, and 30 edited books under his name. He is currently the Editor-in-Chief of Communications in Statistics published by Taylor & Francis. He was also the Editor-in-Chief for the revised version of Encyclopedia of Statistical Sciences published by John Wiley & Sons. He is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. In 2016, he was awarded an Honorary Doctorate from The National and Kapodistrian University of Athens, Athens, Greece. In 2021, he was elected as a Fellow of the Royal Society of Canada.
Affiliations and expertise
Distinguished University Professor, Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, CanadaEC
Elena Castilla
Elena Castilla is an assistant professor at the Department of Applied Mathematics at Rey Juan Carlos University, in Spain. She obtained her Ph.D, M.Sc. and Bachelor Degrees in Mathematics and Statistics at Universidad Complutense de Madrid, and is an awardee of the Ramiro Melendreras Award (SEIO, 2021) and Vicent Caselles Award (RSME & Fundación BBVA 2022). Dr. Castilla’s research interests include information theory, categorical data analysis, composite likelihood, logistic regression models, reliability analysis and robust statistics.
Affiliations and expertise
Assistant Professor, Rey Juan Carlos University, Madrid, SpainRead Statistical Modeling and Robust Inference for One-shot Devices on ScienceDirect