| 书目名称 | Model-Based Recursive Partitioning with Adjustment for Measurement Error | | 副标题 | Applied to the Cox’s | | 编辑 | Hanna Birke | | 视频video | | | 概述 | Publication in the field of natural science.Includes supplementary material: | | 丛书名称 | BestMasters | | 图书封面 |  | | 描述 | 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. | | 出版日期 | Book 2015 | | 关键词 | Biometric; MOB; Measurement Error Modelling; Partitioning Methods; Regression Models | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-658-08505-6 | | isbn_softcover | 978-3-658-08504-9 | | isbn_ebook | 978-3-658-08505-6Series ISSN 2625-3577 Series E-ISSN 2625-3615 | | issn_series | 2625-3577 | | copyright | Springer Fachmedien Wiesbaden 2015 |
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发表于 2025-3-21 18:15:27
