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Bog, hardback First Hitting Time Regression Models af Chrysseis Caroni

First Hitting Time Regression Models

- Lifetime Data Analysis Based on Underlying Stochastic Processes

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This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods.

This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying proce... Læs mere

Produktdetaljer:

Sprog:
Engelsk
ISBN-13:
9781848218895
Sideantal:
150
Udgivet:
12-07-2017
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31-10-2017
 
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Forlagets beskrivelse
This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods.
Bibliotekernes beskrivelse
This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods. In contrast to the rich statistical literature, the regression methods actually employed in lifetime data analysis are limited, particularly in the biomedical field where D. R. Cox s famous semi-parametric proportional hazards model predominates. Practitioners should become familiar with more flexible models. The first hitting time regression models (or threshold regression) presented here represent observed events as the outcome of an underlying stochastic process. One example is death occurring when the patient s health status falls to zero, but the idea has wide applicability in biology, engineering, banking and finance, and elsewhere. The central topic is the model based on an underlying Wiener process, leading to lifetimes following the inverse Gaussian distribution. Introducing time-varying covariates and many other extensions are considered. Various applications are presented in detail.

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