This book offers a practical, application–based approach to flowgraph models for time–to–event data. It clearly shows how this innovative new methodology can be used to analyze data from semi–Markov processes without prior knowledge of stochastic processes––opening the door to interesting applications in survival analysis and reliability as well as stochastic processes.
Unlike other books on multistate time–to–event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes:
- Clear instructions on how to model multistate time–to–event data using flowgraph models
- An emphasis on computation, real data, and Bayesian methods for problem solving
- Real–world examples for analyzing data from stochastic processes
- The use of flowgraph models to analyze complex stochastic networks
- Exercise sets to reinforce the practical approach of this volume
Flowgraph Models for Multistate Time–to–Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.
1. Multistate Models and Flowgraph Models.
2. Flowgraph Models.
3. Inversion of Flowgraph Moment Generating Functions.
4. Censored Data Histograms.
5. Bayesian Prediction for Flowgraph Models.
6. Computation Implementation of Flowgraph Models.
7. Semi–Markov Processes.
8. Incomplete Data.
9. Flowgraph Models for Queuing Systems.
Appendix: Moment Generating Functions.