Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information
To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/24248/ http://journalarticle.ukm.my/24248/1/ST%2016.pdf |
| _version_ | 1848816049618681856 |
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| author | Asghar, Amber Sanaullah, Aamir Hanif, Muhammad Al-Essa, Laila A. |
| author_facet | Asghar, Amber Sanaullah, Aamir Hanif, Muhammad Al-Essa, Laila A. |
| author_sort | Asghar, Amber |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector using multi-auxiliary variables is a less explored area in current literature. This paper addresses the estimation of vector of unknown population variances for multiple study variables by using an estimated vector of variances derived from multi-auxiliary information. This approach is particularly relevant when population variances for the multi-auxiliary variables are not known prior to the survey. The paper introduces a generalized variance and a vector of biases for the proposed multivariate estimator. Special cases of the proposed multivariate variance estimator are provided, accompanied by expressions for mean square errors. Theoretical mathematical conditions are discussed to guide the preference for the proposed estimator. Through the analysis of real-world application-based data, the applicability and efficiency of the proposed multivariate variance estimator are demonstrated, outperforming modified versions of multivariate variance estimators. Additionally, a simulation study validates the superior performance of the proposed estimator compared to its modified estimators. |
| first_indexed | 2025-11-15T00:59:42Z |
| format | Article |
| id | oai:generic.eprints.org:24248 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:59:42Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:242482024-09-24T08:41:17Z http://journalarticle.ukm.my/24248/ Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information Asghar, Amber Sanaullah, Aamir Hanif, Muhammad Al-Essa, Laila A. To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector using multi-auxiliary variables is a less explored area in current literature. This paper addresses the estimation of vector of unknown population variances for multiple study variables by using an estimated vector of variances derived from multi-auxiliary information. This approach is particularly relevant when population variances for the multi-auxiliary variables are not known prior to the survey. The paper introduces a generalized variance and a vector of biases for the proposed multivariate estimator. Special cases of the proposed multivariate variance estimator are provided, accompanied by expressions for mean square errors. Theoretical mathematical conditions are discussed to guide the preference for the proposed estimator. Through the analysis of real-world application-based data, the applicability and efficiency of the proposed multivariate variance estimator are demonstrated, outperforming modified versions of multivariate variance estimators. Additionally, a simulation study validates the superior performance of the proposed estimator compared to its modified estimators. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24248/1/ST%2016.pdf Asghar, Amber and Sanaullah, Aamir and Hanif, Muhammad and Al-Essa, Laila A. (2024) Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information. Sains Malaysiana, 53 (7). pp. 1693-1702. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num7_2024/contentsVol53num7_2024.html |
| spellingShingle | Asghar, Amber Sanaullah, Aamir Hanif, Muhammad Al-Essa, Laila A. Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title | Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title_full | Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title_fullStr | Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title_full_unstemmed | Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title_short | Enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| title_sort | enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information |
| url | http://journalarticle.ukm.my/24248/ http://journalarticle.ukm.my/24248/ http://journalarticle.ukm.my/24248/1/ST%2016.pdf |