Analysis of Incomplete Survey Data - Multiple Imputation via Bayesian Bootstrap Predictive Mean Matching

Koller-Meinfelder, Florian

Analysis of Incomplete Survey Data - Multiple Imputation via Bayesian Bootstrap Predictive Mean Matching

Koller-Meinfelder, Florian
Bamberg : opus, 2009

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Professorship/Faculty:	Fakultät Sozial- und Wirtschaftswissenschaften: Abschlussarbeiten
Author(s):	Meinfelder, Florian
By:	Koller-Meinfelder, Florian
Alternative Title:	Analyse unvollständiger Befragungsdaten - Multiple Imputation mittels Bayesian Bootstrap Predictive Mean Matching
Publisher Information:	Bamberg : opus
Year of publication:	2009
Pages:	XVII, 145
Supervisor(s):	Rässler, Susanne; Raghunathan, Trivellore E.
Language(s):	English
Remark:	Bamberg, Univ., Diss., 2009
Licence:	German Act on Copyright
URN:	urn:nbn:de:bvb:473-opus-2251
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.
SWD Keywords:	Bayes-Entscheidungstheorie; Bootstrap-Statistik; Imputationstechnik; Online-Publikation
Keywords:	Datenergänzung , Multiple Imputation, missing data , multiple imputation , nearest neighbour matching, Datenergänzung, Multiple Imputation, missing data, multiple imputation, nearest neighbour matching
DDC Classification:	510 Mathematics
RVK Classification:	QH 233 QH 235
Document Type:	Doctoralthesis
URI:	https://fis.uni-bamberg.de/handle/uniba/213
Year of publication:	25. January 2010
Awards:	Promotionspreise der Hans-Löwel-Stiftung