?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.
| ISBN: | 9783658085049 |
| Publication date: | 11th February 2015 |
| Author: | Hanna Birke |
| Publisher: | Springer Spektrum an imprint of Springer Fachmedien Wiesbaden |
| Format: | Paperback |
| Pagination: | 240 pages |
| Series: | BestMasters |
| Genres: |
Numerical analysis Computational biology / bioinformatics Oncology |
?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.
Model-Based Recursive Partitioning With Adjustment for Measurement Error features in the following genres: Numerical analysis, Computational biology / bioinformatics, Oncology
Paperback. Not Available.
Model-Based Recursive Partitioning With Adjustment for Measurement Error was written by Hanna Birke and published by Springer Spektrum an imprint of Springer Fachmedien Wiesbaden
Model-Based Recursive Partitioning With Adjustment for Measurement Error has 240 pages
Yes it is part of BestMasters series