Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling
It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provi...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/6/558 |
| _version_ | 1797596125179412480 |
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| author | Shashi Bhushan Anoop Kumar Tolga Zaman Aned Al Mutairi |
| author_facet | Shashi Bhushan Anoop Kumar Tolga Zaman Aned Al Mutairi |
| author_sort | Shashi Bhushan |
| collection | DOAJ |
| description | It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provide some efficient difference- and ratio-type imputation methods in the presence of missing values under RSS. The envisaged imputation methods are demonstrated to provide better results than the existing imputation methods. The theoretical results are enhanced by a computational analysis using real and hypothetically generated symmetric (Normal) and asymmetric (Gamma and Weibull) populations. The computational results show that the proposed imputation method outperforms the existing imputation methods in terms of its higher percent relative efficiency. Additionally, the impact of skewness and kurtosis on the efficiency of the suggested imputation methods has also been calculated. |
| first_indexed | 2024-03-11T02:47:07Z |
| format | Article |
| id | doaj.art-c3c1be6e5adc41d88b1d1d8658221eab |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T02:47:07Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-c3c1be6e5adc41d88b1d1d8658221eab2023-11-18T09:16:44ZengMDPI AGAxioms2075-16802023-06-0112655810.3390/axioms12060558Efficient Difference and Ratio-Type Imputation Methods under Ranked Set SamplingShashi Bhushan0Anoop Kumar1Tolga Zaman2Aned Al Mutairi3Department of Statistics, University of Lucknow, Lucknow 226007, IndiaDepartment of Statistics, Amity University, Lucknow 226028, IndiaDepartment of Statistics, Faculty of Science, Çankiri Karatekin University, Çankiri 18100, TurkeyDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaIt is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provide some efficient difference- and ratio-type imputation methods in the presence of missing values under RSS. The envisaged imputation methods are demonstrated to provide better results than the existing imputation methods. The theoretical results are enhanced by a computational analysis using real and hypothetically generated symmetric (Normal) and asymmetric (Gamma and Weibull) populations. The computational results show that the proposed imputation method outperforms the existing imputation methods in terms of its higher percent relative efficiency. Additionally, the impact of skewness and kurtosis on the efficiency of the suggested imputation methods has also been calculated.https://www.mdpi.com/2075-1680/12/6/558biasmean square errormissing dataimputationranked set sampling |
| spellingShingle | Shashi Bhushan Anoop Kumar Tolga Zaman Aned Al Mutairi Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling Axioms bias mean square error missing data imputation ranked set sampling |
| title | Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling |
| title_full | Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling |
| title_fullStr | Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling |
| title_full_unstemmed | Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling |
| title_short | Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling |
| title_sort | efficient difference and ratio type imputation methods under ranked set sampling |
| topic | bias mean square error missing data imputation ranked set sampling |
| url | https://www.mdpi.com/2075-1680/12/6/558 |
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