Model-Based Recursive Partitioning with Adjustment for Measurement Error

Applied to the Cox¿s Proportional Hazards and Weibull Model
2015
ISBN: 978-3-658-08504-9
Verlag: Springer

Buch, Englisch, 240 Seiten, Paperback, Format (B × H): 148 mm x 210 mm, Gewicht: 3451 g

Reihe: BestMasters

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.

Zielgruppe

Research

Autoren/Hrsg.

Birke, Hanna

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

MOB and Measurement Error Modelling.- Derivation of an Adjusted MOB Algorithm for Covariates Measured with Error
for the Cox and Weibull Model.- Implementation of the Suggested Method for the Weibull Model in the Open-Source
Programming Language R.- Simulation Study Showing the Performance of the Implemented Method.

Über Autor(innen)

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.