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

Search by :

ALL Author Subject ISBN/ISSN Advanced Search

Last search:

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

Text

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

BIRKE, Hanna - Personal Name;

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.

Availability

No copy data

Detail Information

Series Title: BestMasters
Call Number: -
Publisher: Fachmedien Wiesbaden : Springer Spektrum Wiesbaden., 2015
Collation: XXIV, 240; 65 b/w illustrations
Language: English
ISBN/ISSN: 978-3-658-08504-9
Classification: NONE
Content Type: text
Media Type: computer
Carrier Type: online resource
Edition: 1
Subject(s): Cancer Biology
Computational Mathematics and Numerical Analysis
Mathematical and Computational Biology
Specific Detail Info: -
Statement of Responsibility: Hanna Birke

Other Information

Cataloger: SUCI
Source: -
Validator: TAUFIK

Other version/related

No other version available

File Attachment

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

Comments

You must be logged in to post a comment