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200 10$aModel-Based Recursive Partitioning with Adjustment for Measurement Error $eApplied to the Cox?s Proportional Hazards and Weibull Model /$fby Hanna Birke
205   $a1st ed. 2015.
210  1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Spektrum,$d2015.
215   $a1 online resource (259 p.)
225 1 $aBestMasters,$x2625-3615
300   $aDescription based upon print version of record.
311   $a3-658-08504-5 
320   $aIncludes bibliographical references.
327   $aMOB 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.
330   $aModel-based recursive partitioning (MOB) provides a powerful synthesis between machine-learning inspired recursive partitioning methods and regression models. Hanna Birke extends this approach by allowing in addition for measurement error in covariates, as frequently occurring in biometric (or econometric) studies, for instance, when measuring blood pressure or caloric intake per day. After an introduction into the background, the extended methodology is developed in detail for the Cox model and the Weibull model, carefully implemented in R, and investigated in a comprehensive simulation study. Contents MOB and Measurement Error Modelling Derivation of an Adjusted MOB Algorithm for Covariates Measured with Error for the Cox and Weibull Model Implementation of the Suggested Method for the Weibull Model in the Open-Source Programming Language R Simulation Study Showing the Performance of the Implemented Method Target Groups Researchers and students in the fields of statistics and cognate disciplines with interest in advanced modelling in combination with measurement error in covariates Data analysts of complex biometric or econometric studies with variables that are difficult to measure in practice The Author Hanna Birke wrote her master thesis under the supervision of Prof. Dr. Thomas Augustin at the department of statistics of the LMU Munich and is currently working on her doctoral thesis.  .
410  0$aBestMasters,$x2625-3615
606   $aMathematics$xData processing
606   $aBiomathematics
606   $aCancer
606   $aComputational Mathematics and Numerical Analysis
606   $aMathematical and Computational Biology
606   $aCancer Biology
615  0$aMathematics$xData processing.
615  0$aBiomathematics.
615  0$aCancer.
615 14$aComputational Mathematics and Numerical Analysis.
615 24$aMathematical and Computational Biology.
615 24$aCancer Biology.
676   $a510
676   $a518
676   $a570.285
676   $a614.5999
700   $aBirke$b Hanna$4aut$4http://id.loc.gov/vocabulary/relators/aut$0963855
906   $aBOOK
912   $a9910304131903321
996   $aModel-Based Recursive Partitioning with Adjustment for Measurement Error$92185869
997   $aUNINA