New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data
Water shortage could play an imperative role in the future due to an influx of water demand when compared to water supplies. Inadequate water could damage human life and other aspects related to living. This serious issue can be prevented by estimating the demand for water to bridge the small gap be...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-12-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/14551.pdf |
| _version_ | 1827610645872246784 |
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| author | Chugiat Ponkaew Nuanpan Lawson |
| author_facet | Chugiat Ponkaew Nuanpan Lawson |
| author_sort | Chugiat Ponkaew |
| collection | DOAJ |
| description | Water shortage could play an imperative role in the future due to an influx of water demand when compared to water supplies. Inadequate water could damage human life and other aspects related to living. This serious issue can be prevented by estimating the demand for water to bridge the small gap between demand and supplies for water. Some water consumption data recorded daily may be missing and could affect the estimated value of water demand. In this article, new ratio estimators for estimating population total are proposed under unequal probability sampling without replacement when data are missing. Two situations are considered: known or unknown mean of an auxiliary variable and missing data are missing at random for both study and auxiliary variables. The variance and associated estimators of the proposed estimators are investigated under a reverse framework. The proposed estimators are applied to data from simulation studies and empirical data on water demand in Thailand which contain some missing values, to assess the efficacies of the estimators. |
| first_indexed | 2024-03-09T07:53:04Z |
| format | Article |
| id | doaj.art-e00a539871fd4effbe4ed058c5d9a0f1 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:53:04Z |
| publishDate | 2022-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-e00a539871fd4effbe4ed058c5d9a0f12023-12-03T01:26:26ZengPeerJ Inc.PeerJ2167-83592022-12-0110e1455110.7717/peerj.14551New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing dataChugiat Ponkaew0Nuanpan Lawson1Department of Mathematics, Faculty of Science and Technology, Phetchabun Rajabhat University, Phetchabun, ThailandDepartment of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangsue, Bangkok, ThailandWater shortage could play an imperative role in the future due to an influx of water demand when compared to water supplies. Inadequate water could damage human life and other aspects related to living. This serious issue can be prevented by estimating the demand for water to bridge the small gap between demand and supplies for water. Some water consumption data recorded daily may be missing and could affect the estimated value of water demand. In this article, new ratio estimators for estimating population total are proposed under unequal probability sampling without replacement when data are missing. Two situations are considered: known or unknown mean of an auxiliary variable and missing data are missing at random for both study and auxiliary variables. The variance and associated estimators of the proposed estimators are investigated under a reverse framework. The proposed estimators are applied to data from simulation studies and empirical data on water demand in Thailand which contain some missing values, to assess the efficacies of the estimators.https://peerj.com/articles/14551.pdfWater demandPopulation totalUnequal probability sampling without replacementTaylor linearization approachNonlinear estimatorLogistic regression |
| spellingShingle | Chugiat Ponkaew Nuanpan Lawson New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data PeerJ Water demand Population total Unequal probability sampling without replacement Taylor linearization approach Nonlinear estimator Logistic regression |
| title | New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data |
| title_full | New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data |
| title_fullStr | New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data |
| title_full_unstemmed | New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data |
| title_short | New estimators for estimating population total: an application to water demand in Thailand under unequal probability sampling without replacement for missing data |
| title_sort | new estimators for estimating population total an application to water demand in thailand under unequal probability sampling without replacement for missing data |
| topic | Water demand Population total Unequal probability sampling without replacement Taylor linearization approach Nonlinear estimator Logistic regression |
| url | https://peerj.com/articles/14551.pdf |
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