Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2288
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study ; volume:19 ; number:1 ; day:10 ; month:1 ; year:2019 ; pages:1-14 ; date:12.2019
BMC medical research methodology ; 19, Heft 1 (10.1.2019), 1-14, 12.2019
- Creator
-
De Silva, Anurika Priyanjali
Moreno-Betancur, Margarita
De Livera, Alysha Madhu
Lee, Katherine Jane
Simpson, Julie Anne
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s12874-018-0653-0
- URN
-
urn:nbn:de:101:1-2019021622562621922564
- Rights
-
Open Access; 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
- De Silva, Anurika Priyanjali
- Moreno-Betancur, Margarita
- De Livera, Alysha Madhu
- Lee, Katherine Jane
- Simpson, Julie Anne
- SpringerLink (Online service)
Other Objects (12)
Comparison of imputation methods for handling missing categorical data with univariate pattern
A nonparametric multiple imputation approach for missing categorical data
Calibrated imputation for multivariate categorical data
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
Decision Trees for the Imputation of Categorical Data
Missing Data, Imputation, and Endogeneity
Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable
Comparison of imputation methods for univariate categorical longitudinal data
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs
Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review
Comparison of imputation methods for handling missing categorical data with univariate pattern
A nonparametric multiple imputation approach for missing categorical data
Calibrated imputation for multivariate categorical data
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
Decision Trees for the Imputation of Categorical Data
Missing Data, Imputation, and Endogeneity
Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable
Comparison of imputation methods for univariate categorical longitudinal data
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs
Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review
Comparison of imputation methods for handling missing categorical data with univariate pattern
A nonparametric multiple imputation approach for missing categorical data
Calibrated imputation for multivariate categorical data
Multiple imputation for handling missing outcome data when estimating the relative risk
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
Decision Trees for the Imputation of Categorical Data
Missing Data, Imputation, and Endogeneity
Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable
Comparison of imputation methods for univariate categorical longitudinal data
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Missing value imputation for epistatic MAPs