Analysis of Incomplete Survey Data : Multiple Imputation via Bayesian Bootstrap Predictive Mean Matching
Koller-Meinfelder, Florian (2009): Analysis of Incomplete Survey Data : Multiple Imputation via Bayesian Bootstrap Predictive Mean Matching, Bamberg: opus.
Faculty/Chair:
Author:
By:
Koller-Meinfelder, Florian
Alternative Title:
Analyse unvollständiger Befragungsdaten - Multiple Imputation mittels Bayesian Bootstrap Predictive Mean Matching
Publisher Information:
Year of publication:
2009
Pages:
Supervisor:
 ; 
Raghunathan, Trivellore E.
Language:
English
Remark:
Bamberg, Univ., Diss., 2009
Licence:
URN:
Abstract:
Multiple Imputation (MI) is a general purpose approach to impute partially incomplete data. The proposed method - Bayesian Bootstrap Predictive Mean Matching - is a variant that incorporates the robustifying properties of a nearest neighbour technique (Predictive Mean Matching) into MI.
Multiple Imputation (MI) ist ein allgemeiner Ansatz zur Ergänzung fehlender Daten. Die vorgestellte Methode - Bayesian Bootstrap Predictive Mean Matching - ist eine MI-Variante, welche die robustifizierenden Eigenschaften eines Nearest-Neighbour-Verfahrens (Predictive Mean Matching) integriert.
GND Keywords:
Bayes-Entscheidungstheorie
 ; 
Bootstrap-Statistik
 ; 
Imputationstechnik
Keywords:
Datenergänzung , Multiple Imputation
 ; 
missing data , multiple imputation , nearest neighbour matching
 ; 
Datenergänzung
 ; 
Multiple Imputation
 ; 
missing data
 ; 
multiple imputation
 ; 
nearest neighbour matching
DDC Classification:
RVK Classification:
QH 233     QH 235    
Type:
Doctoralthesis
URI:
Activation date:
January 25, 2010
Awards:

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