Quasi Model-Assisted Estimators under Nonresponse in Sample Surveys
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917136024207360 |
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| author | Hasler, Caren Eustache, Esther |
| author_facet | Hasler, Caren Eustache, Esther |
| contents | In the presence of auxiliary information, model-assisted estimators rely on a working model linking the variable of interest to the auxiliary variables in order to improve the efficiency of the Horvitz-Thompson estimator. Model-assisted estimators cannot be directly computed with nonresponse since the values of the variable of interest is missing for a part of the sample units. In this article, we present and study a class of quasi-model-assisted estimators that extend model-assisted estimators to settings with non-ignorable nonresponse. These estimators combine a working model and a response model. The former is used to improve the efficiency, the latter to reweight the nonrespondents. A wide range of statistical learning methods can be used to estimate either of these models. We show that several well-known existing estimators are particular cases of quasi-model-assisted estimators. We examine the behavior of these estimators through a simulation study. The results illustrate how these estimators remain competitive in terms of bias and variance when one of the two models is poorly specified. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_04621 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Quasi Model-Assisted Estimators under Nonresponse in Sample Surveys Hasler, Caren Eustache, Esther Methodology In the presence of auxiliary information, model-assisted estimators rely on a working model linking the variable of interest to the auxiliary variables in order to improve the efficiency of the Horvitz-Thompson estimator. Model-assisted estimators cannot be directly computed with nonresponse since the values of the variable of interest is missing for a part of the sample units. In this article, we present and study a class of quasi-model-assisted estimators that extend model-assisted estimators to settings with non-ignorable nonresponse. These estimators combine a working model and a response model. The former is used to improve the efficiency, the latter to reweight the nonrespondents. A wide range of statistical learning methods can be used to estimate either of these models. We show that several well-known existing estimators are particular cases of quasi-model-assisted estimators. We examine the behavior of these estimators through a simulation study. The results illustrate how these estimators remain competitive in terms of bias and variance when one of the two models is poorly specified. |
| title | Quasi Model-Assisted Estimators under Nonresponse in Sample Surveys |
| topic | Methodology |
| url | https://arxiv.org/abs/2208.04621 |