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Record Nr. |
UNINA9910304131903321 |
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Autore |
Birke Hanna |
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Titolo |
Model-Based Recursive Partitioning with Adjustment for Measurement Error [[electronic resource] ] : Applied to the Cox’s Proportional Hazards and Weibull Model / / by Hanna Birke |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Spektrum, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (259 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Computer mathematics |
Biomathematics |
Cancer research |
Computational Mathematics and Numerical Analysis |
Mathematical and Computational Biology |
Cancer Research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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