First Hitting Time Regression Models

Lifetime Data Analysis Based on Underlying Stochastic Processes

First Hitting Time Regression Models

Chrysseis Caroni, National Technical University of Athens, Greece

ISBN : 9781848218895

Publication Date : July 2017

Hardcover 200 pp

110.00 USD



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.


1. Introduction to Lifetime Data and Regression Models.
2. First Hitting Time Regression Models.
3. Model Fitting and Diagnostics.
4. Extensions to Inverse Gaussian First Hitting Time Regression Models.
5. Relationship of First Hitting Time Models to Proportional Hazards and Accelerated Failure Time Models.
6. Applications.

About the authors/editors

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.

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