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MORE ABOUT THIS BOOK
Main description:
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.
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.
PRODUCT DETAILS
Publisher: Springer (Springer Spektrum)
Publication date: February, 2015
Pages: 264
Weight: 3451g
Availability: Available
Subcategories: Oncology
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