Flowgraph Models for Multistate Time-to-Event Data / Wiley Series in Probability and Statistics (PDF)
(Sprache: Englisch)
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...
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...
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Produktinformationen zu „Flowgraph Models for Multistate Time-to-Event Data / Wiley Series in Probability and Statistics (PDF)“
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.
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.
Inhaltsverzeichnis zu „Flowgraph Models for Multistate Time-to-Event Data / Wiley Series in Probability and Statistics (PDF)“
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.
Autoren-Porträt von Aparna V. Huzurbazar
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.
Bibliographische Angaben
- Autor: Aparna V. Huzurbazar
- 2004, 1. Auflage, 290 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0471686530
- ISBN-13: 9780471686538
- Erscheinungsdatum: 19.11.2004
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