Statistical Modeling and Robust Inference for One-shot Devices

Balakrishnan Narayanaswamy
Heftet / 2025 / Engelsk

Produktdetaljer

ISBN
9780443141539
Publisert
2025-04-01
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
250

Forfatter
Balakrishnan Narayanaswamy

Om bidragsyterne

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. 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.

Statistical Modeling and Robust Inference for One-shot Devices

Balakrishnan Narayanaswamy
Heftet / 2025 / Engelsk
Balakrishnan Narayanaswamy
Heftet / 2025 / Engelsk
Nettpris:
2.326,-
Kommer i salg 01.04.2025
Sjekk lagerstatus i butikk
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.
Les mer
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
Les mer
Explores the statistical modeling of robust inference techniques for testing one-shot device data
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
Les mer

Relaterte produkter

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.
Les mer
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
Les mer
Explores the statistical modeling of robust inference techniques for testing one-shot device data
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
Les mer

Produktdetaljer

ISBN
9780443141539
Publisert
2025-04-01
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
250

Forfatter
Balakrishnan Narayanaswamy

Om bidragsyterne

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. 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.

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