Huzurbazar Flowgraph Models for Multistate Time-to-Event Data

1. Auflage 2004
ISBN: 978-0-471-68653-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 290 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

A unique introduction to the innovative methodology of statisticalflowgraphs
This book offers a practical, application-based approach toflowgraph models for time-to-event data. It clearly shows how thisinnovative new methodology can be used to analyze data fromsemi-Markov processes without prior knowledge of stochasticprocesses--opening the door to interesting applications in survivalanalysis and reliability as well as stochastic processes.
Unlike other books on multistate time-to-event data, this workemphasizes reliability and not just biostatistics, illustratingeach method with medical and engineering examples. It demonstrateshow flowgraphs bring together applied probability techniques andcombine them with data analysis and statistical methods to answerquestions of practical interest. Bayesian methods of data analysisare emphasized. Coverage includes:
* Clear instructions on how to model multistate time-to-event datausing flowgraph models
* An emphasis on computation, real data, and Bayesian methods forproblem solving
* Real-world examples for analyzing data from stochasticprocesses
* The use of flowgraph models to analyze complex stochasticnetworks
* Exercise sets to reinforce the practical approach of thisvolume
Flowgraph Models for Multistate Time-to-Event Data is an invaluableresource/reference for researchers in biostatistics/survivalanalysis, systems engineering, and in fields that use stochasticprocesses, including anthropology, biology, psychology, computerscience, and engineering.

Autoren/Hrsg.

Huzurbazar, Aparna V.

Inhaltsverzeichnis

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.

Über Autor(innen)

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.