Flowgraph Models for Multistate Time-to-Event Data PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Flowgraph Models for Multistate Time-to-Event Data PDF full book. Access full book title Flowgraph Models for Multistate Time-to-Event Data by Aparna V. Huzurbazar. Download full books in PDF and EPUB format.

Flowgraph Models for Multistate Time-to-Event Data

Flowgraph Models for Multistate Time-to-Event Data PDF Author: Aparna V. Huzurbazar
Publisher: John Wiley & Sons
ISBN: 0471686530
Category : Mathematics
Languages : en
Pages : 320

Book Description
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.