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Aggiungi al carrelloPaperback. Condizione: Brand New. 2015 edition. 264 pages. 8.00x5.75x0.75 inches. In Stock.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Die ersten beiden Sätze des Kurztextes für den Umschlag sollen die wichtigsten Vorteile und Kernaussagen Ihres Buches herausstellen. Einleitungssätze wie In den letzten Jahren o.Ä. sollen vermieden werden. Dies dient dazu, dem Buch die bestmögliche Aufmerksamkeit und Auffindbarkeit zu gewährleisten. Bei Google und Amazon werden beispielsweise häufig nur Vorschauen angezeigt. Daher ist es wichtig, die Verkaufsargumente direkt in den ersten beiden Sätzen, die in einer solchen Vorschau erscheinen würden, zu platzieren. Auch lesen viele potenzielle Käufer nicht den ganzen Umschlagtext, sondern nur die ersten 1 bis 2 Sätze. Am Ende des Kurztextes kann gern noch ein 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.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden Feb 2015, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -¿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.Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg 264 pp. Englisch.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Model-Based Recursive Partitioning with Adjustment for Measurement Error | Applied to the Cox's Proportional Hazards and Weibull Model | Hanna Birke | Taschenbuch | xxiv | Englisch | 2015 | Springer Fachmedien Wiesbaden | EAN 9783658085049 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
Da: Buchpark, Trebbin, Germania
EUR 35,81
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 264 | Sprache: Englisch | Produktart: Bücher | ¿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.
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Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden Feb 2015, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Die ersten beiden Sätze des Kurztextes für den Umschlag sollen die wichtigsten Vorteile und Kernaussagen Ihres Buches herausstellen. Einleitungssätze wie In den letzten Jahren o.Ä. sollen vermieden werden. Dies dient dazu, dem Buch die bestmögliche Aufmerksamkeit und Auffindbarkeit zu gewährleisten. Bei Google und Amazon werden beispielsweise häufig nur Vorschauen angezeigt. Daher ist es wichtig, die Verkaufsargumente direkt in den ersten beiden Sätzen, die in einer solchen Vorschau erscheinen würden, zu platzieren. Auch lesen viele potenzielle Käufer nicht den ganzen Umschlagtext, sondern nur die ersten 1 bis 2 Sätze. Am Ende des Kurztextes kann gern noch ein 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. 264 pp. Englisch.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2015
ISBN 10: 3658085045 ISBN 13: 9783658085049
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Publication in the field of natural scienceModel-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 ad.