Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling ; volume:14 ; number:1 ; day:4 ; month:8 ; year:2024 ; pages:1-15 ; date:12.2024
Scientific reports ; 14, Heft 1 (4.8.2024), 1-15, 12.2024
- Creator
-
Kumar, Anoop
Bhushan, Shashi
Emam, Walid
Tashkandy, Yusra
Khan, M. J. S.
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-024-68940-4
- URN
-
urn:nbn:de:101:1-2410252115066.497078045672
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:31 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
- Kumar, Anoop
- Bhushan, Shashi
- Emam, Walid
- Tashkandy, Yusra
- Khan, M. J. S.
- SpringerLink (Online service)
Other Objects (12)
Estimation methods based on ranked set sampling for the power logarithmic distribution
A comparison of strategies for selecting auxiliary variables for multiple imputation
Practical strategies for handling breakdown of multiple imputation procedures
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random
Evidence-ranked motif identification
Extension of the Proximal Auxiliary Problem Method using Logarithmic-quadratic Distances - Convergence Theory and Numerical Investigations
Logarithmic density and logarithmic statistical convergence
Ranked Enumeration of Minimal Triangulations
Ranked Subtree Prune and Regraft
Exploring Data through Ranked Entities
Mining and Querying Ranked Entitites
Imputation
Estimation methods based on ranked set sampling for the power logarithmic distribution
A comparison of strategies for selecting auxiliary variables for multiple imputation
Practical strategies for handling breakdown of multiple imputation procedures
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random
Evidence-ranked motif identification
Extension of the Proximal Auxiliary Problem Method using Logarithmic-quadratic Distances - Convergence Theory and Numerical Investigations
Logarithmic density and logarithmic statistical convergence
Ranked Enumeration of Minimal Triangulations
Ranked Subtree Prune and Regraft
Exploring Data through Ranked Entities
Mining and Querying Ranked Entitites
Imputation
Estimation methods based on ranked set sampling for the power logarithmic distribution
A comparison of strategies for selecting auxiliary variables for multiple imputation
Practical strategies for handling breakdown of multiple imputation procedures
Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random
Evidence-ranked motif identification
Extension of the Proximal Auxiliary Problem Method using Logarithmic-quadratic Distances - Convergence Theory and Numerical Investigations
Logarithmic density and logarithmic statistical convergence
Ranked Enumeration of Minimal Triangulations
Ranked Subtree Prune and Regraft
Exploring Data through Ranked Entities
Mining and Querying Ranked Entitites