國立虎尾科技大學 |

Model-based recursive partitioning with adjustment for measurement error = 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
正題名/作者:	Model-based recursive partitioning with adjustment for measurement error/ by Hanna Birke.
其他題名:	applied to the Cox's Proportional Hazards and Weibull Model /
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. -
電子資源:	http://dx.doi.org/10.1007/978-3-658-08505-6
ISBN:	9783658085056 (electronic bk.)

Model-based recursive partitioning with adjustment for measurement error = 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:

1065462
Errors-in-variables models.

LC Class. No.: QA278.2

Dewey Class. No.: 519.5

Model-based recursive partitioning with adjustment for measurement error = applied to the Cox's Proportional Hazards and Weibull Model /
LDR:02769nam a2200325 a 4500 001 835851
003 DE-He213
005 20150818111216.0
006 m d
007 cr nn 008maaau
008 160421s2015 gw s 0 eng d
020 $a 9783658085056 (electronic bk.)
020 $a 9783658085049 (paper)
024 7 $a 10.1007/978-3-658-08505-6 $2 doi
035 $a 978-3-658-08505-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.2
072 7 $a PBKS $2 bicssc
072 7 $a MAT006000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA278.2 $b .B619 2015
100 1 $a Birke, Hanna. $3 1065461
245 1 0 $a Model-based recursive partitioning with adjustment for measurement error $h [electronic resource] : $b applied to the Cox's Proportional Hazards and Weibull Model / $c by Hanna Birke.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Spektrum, $c 2015.
300 $a xxiv, 240 p. : $b ill., digital ; $c 24 cm.
490 1 $a BestMasters
505 0 $a 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.
520 $a 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.
650 0 $a Errors-in-variables models. $3 1065462
650 0 $a Regression analysis. $3 569541
650 1 4 $a Mathematics. $3 527692
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 669338
650 2 4 $a Mathematical and Computational Biology. $3 786706
650 2 4 $a Cancer Research. $3 668358
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 1021672
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-08505-6
950 $a Behavioral Science (Springer-11640)