Hochschulschrift
Generating multiply imputed synthetic datasets : theory and implementation
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Bamberg, Univ., Diss., 2009
- Klassifikation
-
Statistik
- Schlagwort
-
Imputationstechnik; Bayes-Inferenz; Datenschutz; Anonymisierung; Online-Publikation
- Urheber
- URN
-
urn:nbn:de:bvb:473-opus-2280
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:22 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Hochschulschrift
Beteiligte
Ähnliche Objekte (12)
New methods for generating significance levels from multiply-imputed data
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods
Stochastic production frontiers with multiply imputed German establishment data
Addressing health disparities using multiply imputed injury surveillance data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
G-Tric: generating three-way synthetic datasets with triclustering solutions
Validation of prediction models based on lasso regression with multiply imputed data
New methods for generating significance levels from multiply-imputed data
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods
Stochastic production frontiers with multiply imputed German establishment data
Addressing health disparities using multiply imputed injury surveillance data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
G-Tric: generating three-way synthetic datasets with triclustering solutions
Validation of prediction models based on lasso regression with multiply imputed data
New methods for generating significance levels from multiply-imputed data
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality
A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods
Stochastic production frontiers with multiply imputed German establishment data
Addressing health disparities using multiply imputed injury surveillance data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
Measuring overeducation with earnings frontiers and multiply imputed censored income data
G-Tric: generating three-way synthetic datasets with triclustering solutions