Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles
Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target population. Techniques to mitigate this selection bias...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14137 |
| _version_ | 1789406614901489664 |
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| author | Rueda García, María del Mar Martínez Puertas, Sergio Castro-Martín, Luis |
| author_facet | Rueda García, María del Mar Martínez Puertas, Sergio Castro-Martín, Luis |
| author_sort | Rueda García, María del Mar |
| collection | DSpace |
| description | Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target population. Techniques to mitigate this selection bias in non-probability samples often involve calibration, propensity score adjustment, or statistical matching. In this article, we consider the problem of estimating the finite population distribution function in the context of non-probability surveys and show how some methodologies formulated for linear parameters can be adapted to this functional parameter, both theoretically and empirically, thus enhancing the accuracy and efficiency of the estimates made. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14137 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-141372023-04-12T19:38:55Z Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles Rueda García, María del Mar Martínez Puertas, Sergio Castro-Martín, Luis nonprobability surveys propensity score adjustment survey sampling poverty measures Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target population. Techniques to mitigate this selection bias in non-probability samples often involve calibration, propensity score adjustment, or statistical matching. In this article, we consider the problem of estimating the finite population distribution function in the context of non-probability surveys and show how some methodologies formulated for linear parameters can be adapted to this functional parameter, both theoretically and empirically, thus enhancing the accuracy and efficiency of the estimates made. 2022-12-20T15:19:39Z 2022-12-20T15:19:39Z 2022-12-12 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/14137 10.3390/math10244726 en https://www.mdpi.com/2227-7390/10/24/4726 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | nonprobability surveys propensity score adjustment survey sampling poverty measures Rueda García, María del Mar Martínez Puertas, Sergio Castro-Martín, Luis Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title | Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title_full | Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title_fullStr | Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title_full_unstemmed | Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title_short | Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles |
| title_sort | methods to counter self-selection bias in estimations of the distribution function and quantiles |
| topic | nonprobability surveys propensity score adjustment survey sampling poverty measures |
| url | http://hdl.handle.net/10835/14137 |
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