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Flowgraph Models for Multistate Time-to-Event Data

ISBN: 978-0-471-26514-6
270 pages
November 2004
Flowgraph Models for Multistate Time-to-Event Data (0471265144) cover image
A unique introduction to the innovative methodology of statistical flowgraphs
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.
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Preface.

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.

References.

Author Index.

Subject Index.

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APARNA V. HUZURBAZAR, PhD, is Associate Professor of Statistics at the University of New Mexico. She is the author of numerous technical articles in such areas as Bayesian statistics, survival analysis, stochastic processes, and applications to biomedical and engineering systems.
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"…this is a well-written book on a novel and interesting approach to multistate modeling." (Biometrics, September 2006)

"This book is one that researchers interested in techniques for multistate models, either in reliability or biometry should look at." (Journal of the American Statistical Association, September 2006)

"…a real addition to the toolbox of both biostatisticians who use survival analysis and reliability engineers who do failure analysis on a regular basis." (Technometrics, February 2006)

“…illustrated with interesting examples…the book is particularly welcome…” (International Statistical Institute, January 2006)

"...a useful...account of the use of flowgraphy or semi-Markov parametric models in both industrial and biological applications." (Journal of Biopharmaceutical Statistics, September/October 2005)

"Methods are explained comprehensively, with extensive examples…data analysts would find valuable examples here for their own applications." (Computing Reviews.com, June 2, 2005)

“Fruitful medical and engineering examples and applications are presented…” (Zentralblatt Math, Vol.1055, No.06, 2005)

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