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Model-based recursive partitioning with adjustment for measurement error[electronic resource] :applied to the Cox's Proportional Hazards and Weibull Model /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5
書名/作者:	Model-based recursive partitioning with adjustment for measurement error : applied to the Cox's Proportional Hazards and Weibull Model // by Hanna Birke.
作者:	Birke, Hanna.
出版者:	Wiesbaden : : Springer Fachmedien Wiesbaden :, 2015.
面頁冊數:	xxiv, 240 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Errors-in-variables models.
標題:	Regression analysis.
標題:	Mathematics.
標題:	Computational Mathematics and Numerical Analysis.
標題:	Mathematical and Computational Biology.
標題:	Cancer Research.
ISBN:	9783658085056 (electronic bk.)
ISBN:	9783658085049 (paper)
內容註:	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.
摘要、提要註:	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. Contents 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 Target Groups Researchers and students in the fields of statistics and cognate disciplines with interest in advanced modelling in combination with measurement error in covariates Data analysts of complex biometric or econometric studies with variables that are difficult to measure in practice The Author Hanna Birke wrote her master thesis under the supervision of Prof. Dr. Thomas Augustin at the department of statistics of the LMU Munich and is currently working on her doctoral thesis.
電子資源:	http://dx.doi.org/10.1007/978-3-658-08505-6

Model-based recursive partitioning with adjustment for measurement error[electronic resource] :applied to the Cox's Proportional Hazards and Weibull Model /
Birke, Hanna.

Model-based recursive partitioning with adjustment for measurement errorapplied to the Cox's Proportional Hazards and Weibull Model /[electronic resource] :by Hanna Birke. - Wiesbaden :Springer Fachmedien Wiesbaden :2015. - xxiv, 240 p. :ill., digital ;24 cm. - BestMasters. - BestMasters..

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.

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. Contents 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 Target Groups Researchers and students in the fields of statistics and cognate disciplines with interest in advanced modelling in combination with measurement error in covariates Data analysts of complex biometric or econometric studies with variables that are difficult to measure in practice The Author Hanna Birke wrote her master thesis under the supervision of Prof. Dr. Thomas Augustin at the department of statistics of the LMU Munich and is currently working on her doctoral thesis.

ISBN: 9783658085056 (electronic bk.)

Standard No.: 10.1007/978-3-658-08505-6doiSubjects--Topical Terms:

605839
Errors-in-variables models.

LC Class. No.: QA278.2

Dewey Class. No.: 519.5

Model-based recursive partitioning with adjustment for measurement error[electronic resource] :applied to the Cox's Proportional Hazards and Weibull Model /
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