Dynamic Modelling of Time-to-Event Processes covers an alternative dynamic modelling approach for studying time-to-event processes. This innovative approach covers some key elements, including the Development of continuous-time state of dynamic time-to-event processes, an Introduction of an idea of discrete-time dynamic intervention processes, Treating a time-to-event process operating/functioning under multiple time-scales formulation of continuous and discrete-time interconnected dynamic system as hybrid dynamic time-to-event process, Utilizing Euler-type discretized schemes, developing theoretical dynamic algorithms, and more.
Additional elements of this process include an Introduction of conceptual and computational state and parameter estimation procedures, Developing multistage a robust mean square suboptimal criterion for state and parameter estimation, and Extending the idea conceptual computational simulation process and applying real datasets.
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. Acknowledgments - p. ix 7. Chapter 1: Some latent dynamic structural elements in survival and reliability analysis - p. 1 8. Chapter 2: Linear deterministic hybrid dynamic modeling of time-to-event processes (LDHDM) - p. 85 9. Chapter 3: Conceptual computational and simulation algorithms – LDHDM - p. 117 10. Chapter 4: Nonlinear deterministic interconnected hybrid dynamic modeling for time-to-event processes – INHDMTTEP - p. 123 11. Chapter 5: Conceptual computational and simulation algorithms for INHDMTTEP - p. 155 12. Chapter 6: Stochastic hybrid dynamic modeling for time-to-event processes – SIHDMTTEP - p. 165 13. Chapter 7: Conceptual computational and simulation algorithms for SIHDMTTEP - p. 209 14. Chapter 8: Application to time-to-event datasets - p. 219 15. Chapter 9: Statistical comparative analysis with existing methods - p. 257 16. Chapter 10: Case studies - p. 265 17. Bibliography - p. 343 18. Index - p. 347
Dynamic Modelling of Time-to-Event Processes covers an alternative dynamic modelling approach for studying time-to-event processes. This innovative approach covers some key elements, including the Development of continuous-time state of dynamic time-to-event processes, an Introduction of an idea of discrete-time dynamic intervention processes, Treating a time-to-event process operating/functioning under multiple time-scales formulation of continuous and discrete-time interconnected dynamic system as hybrid dynamic time-to-event process, Utilizing Euler-type discretized schemes, developing theoretical dynamic algorithms, and more.
Additional elements of this process include an Introduction of conceptual and computational state and parameter estimation procedures, Developing multistage a robust mean square suboptimal criterion for state and parameter estimation, and Extending the idea conceptual computational simulation process and applying real datasets.
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. Acknowledgments - p. ix 7. Chapter 1: Some latent dynamic structural elements in survival and reliability analysis - p. 1 8. Chapter 2: Linear deterministic hybrid dynamic modeling of time-to-event processes (LDHDM) - p. 85 9. Chapter 3: Conceptual computational and simulation algorithms – LDHDM - p. 117 10. Chapter 4: Nonlinear deterministic interconnected hybrid dynamic modeling for time-to-event processes – INHDMTTEP - p. 123 11. Chapter 5: Conceptual computational and simulation algorithms for INHDMTTEP - p. 155 12. Chapter 6: Stochastic hybrid dynamic modeling for time-to-event processes – SIHDMTTEP - p. 165 13. Chapter 7: Conceptual computational and simulation algorithms for SIHDMTTEP - p. 209 14. Chapter 8: Application to time-to-event datasets - p. 219 15. Chapter 9: Statistical comparative analysis with existing methods - p. 257 16. Chapter 10: Case studies - p. 265 17. Bibliography - p. 343 18. Index - p. 347
