Name: First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (en Inglés)
SKU: 48929703
Price: 202.02 USD
Availability: InStock
Author: Caroni, Chrysseis
ISBN: 9781848218895

portada First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (en Inglés)

First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (en Inglés)

Formato

Libro Físico

Autor

Chrysseis Caroni

Editorial

Wiley-Iste

Idioma

Inglés

N° páginas

208

Encuadernación

Tapa Dura

Dimensiones

23.6x15.7x1.5 cm

Peso

0.43 kg.

ISBN13

9781848218895

Categorías

Cálculo y análisis matemático

First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (en Inglés)

Chrysseis Caroni (Autor) · Wiley-Iste · Tapa Dura

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Reseña del libro "First Hitting Time Regression Models: Lifetime Data Analysis Based on Underlying Stochastic Processes (en Inglés)"

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.