Optimal Imputation Methods under Stratified Ranked Set Sampling
It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate i...
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Instituto Nacional de Estatística | Statistics Portugal
2025-02-01
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| Series: | Revstat Statistical Journal |
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| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/501 |
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| author | Shashi Bhushan Anoop Kumar |
| author_facet | Shashi Bhushan Anoop Kumar |
| author_sort | Shashi Bhushan |
| collection | DOAJ |
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It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations.
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| format | Article |
| id | doaj-art-507a8a8045794928b055e28dfae53c88 |
| institution | Kabale University |
| issn | 1645-6726 2183-0371 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj-art-507a8a8045794928b055e28dfae53c882025-02-06T10:52:39ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712025-02-0123110.57805/revstat.v23i1.501Optimal Imputation Methods under Stratified Ranked Set SamplingShashi Bhushan 0Anoop Kumar 1University of LucknowAmity University It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations. https://revstat.ine.pt/index.php/REVSTAT/article/view/501missing valuesimputationstratified ranked set sampling |
| spellingShingle | Shashi Bhushan Anoop Kumar Optimal Imputation Methods under Stratified Ranked Set Sampling Revstat Statistical Journal missing values imputation stratified ranked set sampling |
| title | Optimal Imputation Methods under Stratified Ranked Set Sampling |
| title_full | Optimal Imputation Methods under Stratified Ranked Set Sampling |
| title_fullStr | Optimal Imputation Methods under Stratified Ranked Set Sampling |
| title_full_unstemmed | Optimal Imputation Methods under Stratified Ranked Set Sampling |
| title_short | Optimal Imputation Methods under Stratified Ranked Set Sampling |
| title_sort | optimal imputation methods under stratified ranked set sampling |
| topic | missing values imputation stratified ranked set sampling |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/501 |
| work_keys_str_mv | AT shashibhushan optimalimputationmethodsunderstratifiedrankedsetsampling AT anoopkumar optimalimputationmethodsunderstratifiedrankedsetsampling |