Overview
- Provides the first comprehensive overview of statistical methods for discrete failure times
- Contains numerous examples and exercises that illustrate the presented methods
- Introduces novel methodology for model selection, nonparametric estimation and model evaluation that is new in the context of discrete failure analysis
- Reproducible data through freely available R codes
Part of the book series: Springer Series in Statistics (SSS)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (10 chapters)
-
Front Matter
-
Back Matter
Keywords
- Survival data
- Survival functions
- Discrete hazard function
- Time-to-Event Data
- Life tables
- Discrete hazard model
- Continuation ratio model
- Goodness-of-Fit
- Time-dependent AUC
- discSurv
- Interval censoring
- Recursive partitioning
- Multiple spells
- Competing risks
- Generalized estimation equations
- Sequential methods in item response theory
- Discrete frailty model
- Smooth effects
- Additive models
- Penalized regression
- Gradient boosting
About this book
This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.
Reviews
Authors and Affiliations
About the authors
Bibliographic Information
Book Title: Modeling Discrete Time-to-Event Data
Authors: Gerhard Tutz, Matthias Schmid
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-3-319-28158-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-28156-8Published: 22 June 2016
Softcover ISBN: 978-3-319-80285-5Published: 31 May 2018
eBook ISBN: 978-3-319-28158-2Published: 14 June 2016
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: X, 247
Number of Illustrations: 55 b/w illustrations, 3 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Life Sciences, Medicine, Health Sciences, Statistics for Social Sciences, Humanities, Law, Statistics and Computing/Statistics Programs