Semiparametric fractional imputation using empirical likelihood in survey sampling
The empirical likelihood method is a powerful tool for incorporating moment conditions in statistical inference. We propose a novel application of the empirical likelihood for handling item non-response in survey sampling. The proposed method takes the form of fractional imputation but it does not r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-01-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2017.1328244 |
| _version_ | 1797677061217714176 |
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| author | Sixia Chen Jae kwang Kim |
| author_facet | Sixia Chen Jae kwang Kim |
| author_sort | Sixia Chen |
| collection | DOAJ |
| description | The empirical likelihood method is a powerful tool for incorporating moment conditions in statistical inference. We propose a novel application of the empirical likelihood for handling item non-response in survey sampling. The proposed method takes the form of fractional imputation but it does not require parametric model assumptions. Instead, only the first moment condition based on a regression model is assumed and the empirical likelihood method is applied to the observed residuals to get the fractional weights. The resulting semiparametric fractional imputation provides $\sqrt{n}$-consistent estimates for various parameters. Variance estimation is implemented using a jackknife method. Two limited simulation studies are presented to compare several imputation estimators. |
| first_indexed | 2024-03-11T22:39:33Z |
| format | Article |
| id | doaj.art-1d4646e030df427e9ca10f307cdd7b0d |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:39:33Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-1d4646e030df427e9ca10f307cdd7b0d2023-09-22T09:19:44ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772017-01-0111698110.1080/24754269.2017.13282441328244Semiparametric fractional imputation using empirical likelihood in survey samplingSixia Chen0Jae kwang Kim1University of OklahomaIowa State UniversityThe empirical likelihood method is a powerful tool for incorporating moment conditions in statistical inference. We propose a novel application of the empirical likelihood for handling item non-response in survey sampling. The proposed method takes the form of fractional imputation but it does not require parametric model assumptions. Instead, only the first moment condition based on a regression model is assumed and the empirical likelihood method is applied to the observed residuals to get the fractional weights. The resulting semiparametric fractional imputation provides $\sqrt{n}$-consistent estimates for various parameters. Variance estimation is implemented using a jackknife method. Two limited simulation studies are presented to compare several imputation estimators.http://dx.doi.org/10.1080/24754269.2017.1328244item non-responsemissing dataquantile estimationrobust estimation |
| spellingShingle | Sixia Chen Jae kwang Kim Semiparametric fractional imputation using empirical likelihood in survey sampling Statistical Theory and Related Fields item non-response missing data quantile estimation robust estimation |
| title | Semiparametric fractional imputation using empirical likelihood in survey sampling |
| title_full | Semiparametric fractional imputation using empirical likelihood in survey sampling |
| title_fullStr | Semiparametric fractional imputation using empirical likelihood in survey sampling |
| title_full_unstemmed | Semiparametric fractional imputation using empirical likelihood in survey sampling |
| title_short | Semiparametric fractional imputation using empirical likelihood in survey sampling |
| title_sort | semiparametric fractional imputation using empirical likelihood in survey sampling |
| topic | item non-response missing data quantile estimation robust estimation |
| url | http://dx.doi.org/10.1080/24754269.2017.1328244 |
| work_keys_str_mv | AT sixiachen semiparametricfractionalimputationusingempiricallikelihoodinsurveysampling AT jaekwangkim semiparametricfractionalimputationusingempiricallikelihoodinsurveysampling |