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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...

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Main Authors: Asghar, Amber, Sanaullah, Aamir, Hanif, Muhammad, Al-Essa, Laila A.
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/24248/1/ST%2016.pdf
http://journalarticle.ukm.my/24248/
https://www.ukm.my/jsm/english_journals/vol53num7_2024/contentsVol53num7_2024.html
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spelling my-ukm.journal.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
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
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.
format Article
author Asghar, Amber
Sanaullah, Aamir
Hanif, Muhammad
Al-Essa, Laila A.
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
author_facet Asghar, Amber
Sanaullah, Aamir
Hanif, Muhammad
Al-Essa, Laila A.
author_sort Asghar, Amber
title 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_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_sort enhancing precision in population variance vector estimation: a two-phase sampling approach with multi-auxiliary information
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/24248/1/ST%2016.pdf
http://journalarticle.ukm.my/24248/
https://www.ukm.my/jsm/english_journals/vol53num7_2024/contentsVol53num7_2024.html
_version_ 1811683424682901504
score 13.211869

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