First Hitting Time Regression Models

Lifetime Data Analysis Based on Underlying Stochastic Processes

Nonfiction, Science & Nature, Mathematics, Mathematical Analysis
Cover of the book First Hitting Time Regression Models by Chrysseis Caroni, Wiley
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Author: Chrysseis Caroni ISBN: 9781119437222
Publisher: Wiley Publication: July 17, 2017
Imprint: Wiley-ISTE Language: English
Author: Chrysseis Caroni
ISBN: 9781119437222
Publisher: Wiley
Publication: July 17, 2017
Imprint: Wiley-ISTE
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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