Model-Based Recursive Partitioning with Adjustment for Measurement Error: Applied to the Cox’s Proportional Hazards and Weibull Model

✍ Scribed by Hanna Birke (auth.)

Publisher: Springer Spektrum
Year: 2015
Tongue: English
Leaves: 259
Series: BestMasters
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XXIV
Introduction....Pages 1-2
Theoretical Background....Pages 3-33
Implementation....Pages 35-54
Simulation Study....Pages 55-94
Conclusion....Pages 95-233
Back Matter....Pages 235-240

✦ Subjects

Computational Mathematics and Numerical Analysis; Mathematical and Computational Biology; Cancer Research

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