Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Chapter One: Inference for one-shot devices with Weibull component lifetimes under gamma frailty model - p. 1 8. Chapter Two: Signature reliability detection and performance optimization of low-cost autonomous robot vacuum cleaner via-UGFT - p. 35 9. Chapter Three: The n-consistency of the empirical estimator of stationary probability of semi-Markov chains - p. 51 10. Chapter Four: Alternative transient solutions for semi-Markov redundant systems in reliability engineering - p. 63 11. Chapter Five: Parametric estimation of censored semi-Markov chains - p. 83 12. Chapter 6: Understanding a system's performance in the presence of k-out-of-n: F and standby redundancy: a reliability approach through Markov process - p. 109 13. Chapter Seven: On some occurrence rates for Markov processes with application - p. 123 14. Chapter Eight: Interval-censored reliability tests under lognormal lifetimes - p. 139 15. Chapter Nine: Selective review of penalized learning methods for event processes - p. 159 16. Chapter Ten: Hidden Markov model applications - p. 191 17. Chapter Eleven: Stochastic modeling of the elastic properties of carbon-fiber-reinforced 3D printed filaments using polynomial chaos expansion - p. 215 18. Chapter Twelve: Stochastic functionally pooled models for diagnostics and prognostics in engineering - p. 229 19. Chapter Thirteen: The application of drifting Markov modeling to dynamics skill acquisition - p. 261 20. Chapter Fourteen: A multi-granularity smart rejuvenation framework for a two-unit series system - p. 277 21. Chapter Fifteen: Fitting a managed population model using ABC - p. 299 22. Index - p. 315
Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Chapter One: Inference for one-shot devices with Weibull component lifetimes under gamma frailty model - p. 1 8. Chapter Two: Signature reliability detection and performance optimization of low-cost autonomous robot vacuum cleaner via-UGFT - p. 35 9. Chapter Three: The n-consistency of the empirical estimator of stationary probability of semi-Markov chains - p. 51 10. Chapter Four: Alternative transient solutions for semi-Markov redundant systems in reliability engineering - p. 63 11. Chapter Five: Parametric estimation of censored semi-Markov chains - p. 83 12. Chapter 6: Understanding a system's performance in the presence of k-out-of-n: F and standby redundancy: a reliability approach through Markov process - p. 109 13. Chapter Seven: On some occurrence rates for Markov processes with application - p. 123 14. Chapter Eight: Interval-censored reliability tests under lognormal lifetimes - p. 139 15. Chapter Nine: Selective review of penalized learning methods for event processes - p. 159 16. Chapter Ten: Hidden Markov model applications - p. 191 17. Chapter Eleven: Stochastic modeling of the elastic properties of carbon-fiber-reinforced 3D printed filaments using polynomial chaos expansion - p. 215 18. Chapter Twelve: Stochastic functionally pooled models for diagnostics and prognostics in engineering - p. 229 19. Chapter Thirteen: The application of drifting Markov modeling to dynamics skill acquisition - p. 261 20. Chapter Fourteen: A multi-granularity smart rejuvenation framework for a two-unit series system - p. 277 21. Chapter Fifteen: Fitting a managed population model using ABC - p. 299 22. Index - p. 315
