Книга Flowgraph Models for Multistate Time to Event Data Aparna V. Huzurbazar

Flowgraph Models for Multistate Time to Event Data

Автор: Aparna V. Huzurbazar
Език: Английски език
Корици: С твърди корици
Издател: John Wiley & Sons Inc
Наличност: Външен склад
Изпращаме след 9-15 дни
185.72 363.24 лв
A unique introduction to the innovative methodology of statistical flowgraphs This book offers a pra...

Информация за книгата

Език
Английски език
Корици
Книга - С твърди корици
Издадена
2004
страници
270
EAN
9780471265146
ISBN
0471265144
Enbook ID
04889164
Издател
Теглоt
570
Размери
158 x 242 x 22

Пълно описание

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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