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Model-Based Recursive Partitioning with Adjustment for Measurement Error
Applied to the Cox's Proportional Hazards and Weibull Model
Language
English
English
Book
Paperback
Model-based recursive partitioning (MOB) provides a powerful synthesis between machine-learning insp...
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Libristo code: 09136900
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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.
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About the book
Full name
Model-Based Recursive Partitioning with Adjustment for Measurement Error
Author
Hanna Birke
Language
English
English
Binding
Book - Paperback
Date of issue
2015
Number of pages
240
EAN
9783658085049
ISBN
3658085045
Libristo code
09136900
Publishers
Springer Fachmedien Wiesbaden
Weight
3451
Dimensions
148 x 210 x 213
Categories
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