The Impact of multiple imputation for DACSEIS

DSpace Repositorium (Manakin basiert)


Dateien:

Zitierfähiger Link (URI): http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-11352
http://hdl.handle.net/10900/47296
Dokumentart: Arbeitspapier
Erscheinungsdatum: 2004
Sprache: Englisch
Fakultät: 6 Wirtschafts- und Sozialwissenschaftliche Fakultät
Fachbereich: Wirtschaftswissenschaften
DDC-Klassifikation: 310 - Allgemeine Statistiken
Schlagworte: Imputationstechnik , Stichprobenfehler
Freie Schlagwörter: Complexe Stichproben , Monte-Carlo-Simulation , Fehlende Daten
Complex survey , Monte-Carlo techniques , missing data
Weitere beteiligte Personen: Münnich, Ralf (Co-ordinator)
Lizenz: http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=de http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=en
Zur Langanzeige

Abstract:

This paper is designed to provide an extensive introduction to the principles of multiple imputation and to give some general recommendations of using multiple imputation techniques in the DACSEIS universes. The definition of an ignorable missingness mechanism is explained, and the concept of the observed-data likelihood is discussed. To introduce the multiple imputation principle a short introduction of Bayesian statistics is provided. A small simulation study is performed comparing different approaches to illuminate the advantages and disadvantages of different imputation techniques. Finally, an overview about recently available multiple imputation software is given and violations of the assumptions made are addressed.

Das Dokument erscheint in: