Model-based recursive partitioning (MOB) provides a powerful synthesis between machine-learning inspired recursive partitioning methods and regression models. Hanna Birke extends this approach by allowing in addition for measurement error in covariates, as frequently occurring in biometric (or econometric) studies, for instance, when measuring blood pressure or caloric intake per day. After an introduction into the background, the extended methodology is developed in detail for the Cox model and the Weibull model, carefully implemented in R, and investigated in a comprehensive simulation study.

Keywords

Authors and Affiliations

Düsseldorf, Germany

Hanna Birke

About the author

Hanna Birke wrote her master thesis under the supervision of Prof. Dr. Thomas Augustin at the department of statistics of the LMU Munich and is currently working on her doctoral thesis.

Bibliographic Information

Book Title: Model-Based Recursive Partitioning with Adjustment for Measurement Error
Book Subtitle: Applied to the Cox’s Proportional Hazards and Weibull Model
Authors: Hanna Birke
Series Title: BestMasters
DOI: https://doi.org/10.1007/978-3-658-08505-6
Publisher: Springer Spektrum Wiesbaden
eBook Packages: Behavioral Science, Behavioral Science and Psychology (R0)
Copyright Information: Springer Fachmedien Wiesbaden 2015
Softcover ISBN: 978-3-658-08504-9Published: 11 February 2015
eBook ISBN: 978-3-658-08505-6Published: 27 January 2015
Series ISSN: 2625-3577
Series E-ISSN: 2625-3615
Edition Number: 1
Number of Pages: XXIV, 240
Number of Illustrations: 65 b/w illustrations
Topics: Computational Mathematics and Numerical Analysis, Mathematical and Computational Biology, Cancer Research

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Table of contents (5 chapters)

Front Matter

Introduction

Theoretical Background

Implementation

Simulation Study

Conclusion

Back Matter

About this book