First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (Mathematical Models and Methods in Reliability Set)

Caroni, Chrysseis

ISBN 10: 1848218893 / ISBN 13: 9781848218895

Published by Wiley-ISTE, 2017

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Bibliographic Details

• Title: First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (Mathematical Models and Methods in Reliability Set)
• Publisher: Wiley-ISTE
• Publication Date: 2017
• Language: English
• ISBN 10: 1848218893
• ISBN 13: 9781848218895
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

Chrysseis Caroni is Professor in the Department of Mathematics, National Technical University of Athens, Greece. She holds a PhD in Statistics from Southampton University, UK. Her research focuses on reliability and survival analysis, and on methods for detecting outliers.