- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), ISSN: 1467-9876, Volume: 69, Issue: 3, Pages: 547-564
- Event
-
Veröffentlichung
- (where)
-
London
- (who)
-
Royal Statistical Society
- (when)
-
2020
- Event
-
Veröffentlichung
- (where)
-
Berlin
- (who)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (when)
-
2020
- Creator
-
Hammon, Angelina
Zinn, Sabine
- DOI
-
10.1111/rssc.12401
- Handle
-
10419/222432
- URN
-
urn:nbn:de:101:1-2020072804411036251143
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:51 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hammon, Angelina
- Zinn, Sabine
- Royal Statistical Society
- Deutsches Institut für Wirtschaftsforschung (DIW)
Time of origin
- 2020
Other Objects (12)
Multiple imputation of binary multilevel missing not at random data
Multiple imputation of missing data in multilevel research
Multiple imputation methods for missing multilevel ordinal outcomes
Imputation strategies for missing binary outcomes in cluster randomized trials
Multiple imputation of ordinal missing not at random data
Multiple imputation of multilevel missing data: An introduction to the R package pan
Missing Data, Imputation, and Endogeneity
Missing value imputation for epistatic MAPs
Taking 'don't knows' as valid responses: A complete random imputation of missing data
Taking 'don't knows' as valid responses : A complete random imputation of missing data
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random
Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis
Multiple imputation of binary multilevel missing not at random data
Multiple imputation of missing data in multilevel research
Multiple imputation methods for missing multilevel ordinal outcomes
Imputation strategies for missing binary outcomes in cluster randomized trials
Multiple imputation of ordinal missing not at random data
Multiple imputation of multilevel missing data: An introduction to the R package pan
Missing Data, Imputation, and Endogeneity
Missing value imputation for epistatic MAPs
Taking 'don't knows' as valid responses: A complete random imputation of missing data
Taking 'don't knows' as valid responses : A complete random imputation of missing data
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random
Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis
Multiple imputation of binary multilevel missing not at random data
Multiple imputation of missing data in multilevel research
Multiple imputation methods for missing multilevel ordinal outcomes
Imputation strategies for missing binary outcomes in cluster randomized trials
Multiple imputation of ordinal missing not at random data
Multiple imputation of multilevel missing data: An introduction to the R package pan
Missing Data, Imputation, and Endogeneity
Missing value imputation for epistatic MAPs
Taking 'don't knows' as valid responses: A complete random imputation of missing data
Taking 'don't knows' as valid responses : A complete random imputation of missing data
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random