The optimization problem of quantile and poverty measures estimation based on calibration
New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14851 https://doi.org/10.1016/j.cam.2020.113054 |
| _version_ | 1789406553845006336 |
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| author | Martínez Puertas, Sergio Rueda García, María del Mar Illescas Manzano, María Dolores |
| author_facet | Martínez Puertas, Sergio Rueda García, María del Mar Illescas Manzano, María Dolores |
| author_sort | Martínez Puertas, Sergio |
| collection | DSpace |
| description | New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14851 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-148512024-01-08T10:16:39Z The optimization problem of quantile and poverty measures estimation based on calibration Martínez Puertas, Sergio Rueda García, María del Mar Illescas Manzano, María Dolores Optimization calibration technique poverty measure estimates survey sampling New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators. 2023-12-19T09:52:10Z 2023-12-19T09:52:10Z 2020-06-12 info:eu-repo/semantics/article http://hdl.handle.net/10835/14851 https://doi.org/10.1016/j.cam.2020.113054 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Optimization calibration technique poverty measure estimates survey sampling Martínez Puertas, Sergio Rueda García, María del Mar Illescas Manzano, María Dolores The optimization problem of quantile and poverty measures estimation based on calibration |
| title | The optimization problem of quantile and poverty measures estimation based on calibration |
| title_full | The optimization problem of quantile and poverty measures estimation based on calibration |
| title_fullStr | The optimization problem of quantile and poverty measures estimation based on calibration |
| title_full_unstemmed | The optimization problem of quantile and poverty measures estimation based on calibration |
| title_short | The optimization problem of quantile and poverty measures estimation based on calibration |
| title_sort | optimization problem of quantile and poverty measures estimation based on calibration |
| topic | Optimization calibration technique poverty measure estimates survey sampling |
| url | http://hdl.handle.net/10835/14851 https://doi.org/10.1016/j.cam.2020.113054 |
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