Model-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox's Proportional Hazards and Weibull Model by Hanna BirkeModel-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox's Proportional Hazards and Weibull Model by Hanna Birke

Model-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox's…

byHanna Birke

Paperback | February 11, 2015

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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.
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.
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Title:Model-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox's…Format:PaperbackDimensions:240 pagesPublished:February 11, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3658085045

ISBN - 13:9783658085049

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Table of Contents

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.