Estimation of coefficient of variation using calibrated estimators in double stratified random sampling
One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications. The traditional estimators of the coefficient...
| Main Authors: | , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14192 |
| _version_ | 1789406467008233472 |
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| author | Shahzad, Usman Ahmad, Ishfaq García Luengo, Amelia Victoria Zaman, Tolga Al-Noor, Nadia H. Kumar, Anoop |
| author_facet | Shahzad, Usman Ahmad, Ishfaq García Luengo, Amelia Victoria Zaman, Tolga Al-Noor, Nadia H. Kumar, Anoop |
| author_sort | Shahzad, Usman |
| collection | DSpace |
| description | One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications. The traditional estimators of the coefficient of variation are based on conventional moments; therefore, these are highly affected by the presence of extreme values. In this article, we develop some novel calibration-based coefficient of variation estimators for the study variable under double stratified random sampling (DSRS) using the robust features of linear (L and TL) moments, which offer appropriate coefficient of variation estimates. To evaluate the usefulness of the proposed estimators, a simulation study is performed by using three populations out of which one is based on the COVID-19 pandemic data set and the other two are based on apple fruit data sets. The relative efficiency of the proposed estimators with respect to the existing estimators has been calculated. The superiority of the suggested estimators over the existing estimators are clearly validated by using the real data sets. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14192 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-141922023-04-12T19:37:39Z Estimation of coefficient of variation using calibrated estimators in double stratified random sampling Shahzad, Usman Ahmad, Ishfaq García Luengo, Amelia Victoria Zaman, Tolga Al-Noor, Nadia H. Kumar, Anoop coefficient of variation linear moments calibration approach double stratified random sampling One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications. The traditional estimators of the coefficient of variation are based on conventional moments; therefore, these are highly affected by the presence of extreme values. In this article, we develop some novel calibration-based coefficient of variation estimators for the study variable under double stratified random sampling (DSRS) using the robust features of linear (L and TL) moments, which offer appropriate coefficient of variation estimates. To evaluate the usefulness of the proposed estimators, a simulation study is performed by using three populations out of which one is based on the COVID-19 pandemic data set and the other two are based on apple fruit data sets. The relative efficiency of the proposed estimators with respect to the existing estimators has been calculated. The superiority of the suggested estimators over the existing estimators are clearly validated by using the real data sets. 2023-01-26T07:33:02Z 2023-01-26T07:33:02Z 2023-01-03 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/14192 10.3390/math11010252 en https://www.mdpi.com/2227-7390/11/1/252 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | coefficient of variation linear moments calibration approach double stratified random sampling Shahzad, Usman Ahmad, Ishfaq García Luengo, Amelia Victoria Zaman, Tolga Al-Noor, Nadia H. Kumar, Anoop Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title | Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title_full | Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title_fullStr | Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title_full_unstemmed | Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title_short | Estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| title_sort | estimation of coefficient of variation using calibrated estimators in double stratified random sampling |
| topic | coefficient of variation linear moments calibration approach double stratified random sampling |
| url | http://hdl.handle.net/10835/14192 |
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