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

-15% su kodu: ENG15
71,98 
Įprasta kaina: 84,68 
-15% su kodu: ENG15
Kupono kodas: ENG15
Akcija baigiasi: 2025-03-03
-15% su kodu: ENG15
71,98 
Įprasta kaina: 84,68 
-15% su kodu: ENG15
Kupono kodas: ENG15
Akcija baigiasi: 2025-03-03
-15% su kodu: ENG15
2025-02-28 84.6800 InStock
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Knygos aprašymas

¿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.

Informacija

Autorius: Hanna Birke
Serija: BestMasters
Leidėjas: Springer Fachmedien Wiesbaden
Išleidimo metai: 2015
Knygos puslapių skaičius: 264
ISBN-10: 3658085045
ISBN-13: 9783658085049
Formatas: 210 x 148 x 15 mm. Knyga minkštu viršeliu
Kalba: Anglų

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