Imputation of missing data using multi auxiliary information under ranked set sampling

In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of the proposed imputation methods. These methods...

Full description

Saved in:
Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 54; no. 5; pp. 1500 - 1521
Main Authors Bhushan, Shashi, Kumar, Anoop
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 04.05.2025
Taylor & Francis Ltd
Subjects
Imputation
Missing data
Ranked set sampling
Simulation
Online AccessGet full text

Cover

More Information
Summary:In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of the proposed imputation methods. These methods are dominated by the proposed imputation methods. The theoretical comparison provides the dominance conditions of the proposed imputation methods over their conventional counterparts. In support of the theoretical findings, a simulation study is considered over a hypothetically generated population. Furthermore, some real data examples are also provided to generalize the simulation results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2023.2288796