Model-Based Recursive Partitioning with Adjustment for Measurement Error : Applied to the Cox's Proportional Hazards and Weibull Model (Bestmasters) （2015）

Birke, Hanna

Springer Spektrum（2015/02発売）

ただいまウェブストアではご注文を受け付けておりません。 ⇒古書を探す

製本 Paperback:紙装版/ペーパーバック版／ページ数 240 p.
商品コード 9783658085049

Full 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.

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.