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Model-Based Recursive Partitioning with Adjustment for Measurement Error

Applied to the Cox’s Proportional Hazards and Weibull Model

  • Book
  • © 2015

Overview

Authors:
  1. Hanna Birke

    1. Düsseldorf, Germany

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  • Publication in the field of natural science
  • Includes supplementary material: sn.pub/extras

Part of the book series: BestMasters (BEST)

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Table of contents (5 chapters)

  1. Front Matter

    Pages I-XXIV
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  2. Introduction

    • Hanna Birke
    Pages 1-2

  3. Theoretical Background

    • Hanna Birke
    Pages 3-33

  4. Implementation

    • Hanna Birke
    Pages 35-54

  5. Simulation Study

    • Hanna Birke
    Pages 55-94

  6. Conclusion

    • Hanna Birke
    Pages 95-233

  7. Back Matter

    Pages 235-240
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Keywords

About this book

​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.

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.

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