Robust Lavallee-Hidiroglou stratified sampling strategy
There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target...
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| Format: | Article |
| Language: | English |
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European Survey Research Association
2012-12-01
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| Series: | Survey Research Methods |
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| Online Access: | https://ojs.ub.uni-konstanz.de/srm/article/view/5130 |
| _version_ | 1818486034915131392 |
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| author | Maria Caterina Bramati |
| author_facet | Maria Caterina Bramati |
| author_sort | Maria Caterina Bramati |
| collection | DOAJ |
| description | There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target variable Y, the objective of the survey, is unknown, some auxiliary information X known for the entire population from which the sample is drawn, is used. Such information is helpful as it is typically strongly correlated with the target Y. However, some discrepancies between these variables may arise. The use of auxiliary information, combined with the choice of the appropriate statistical model to estimate the relationship between Y and X, is crucial for the determination of the strata bounds, the size of the sample and the sampling rates according to a chosen precision level for the estimates, as has been shown by Rivest (2002). Nevertheless, this regression-based approach is highly sensitive to the presence of contaminated data. Since the key tool for stratified sampling is the measure of scale of Y conditional on the knowledge of the auxiliary X, a robust approach based on the S-estimator of the regression is proposed in this paper. The aim is to allow for robust sample size and strata bounds determination, together with optimal sample allocation. Simulation results based on data from the Construction sector of a Structural Business Survey illustrate the advantages of the proposed method. |
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| format | Article |
| id | doaj.art-d65c8015a16b4d66a110fa4b50c0b3a5 |
| institution | Directory Open Access Journal |
| issn | 1864-3361 |
| language | English |
| last_indexed | 2024-12-10T16:17:33Z |
| publishDate | 2012-12-01 |
| publisher | European Survey Research Association |
| record_format | Article |
| series | Survey Research Methods |
| spelling | doaj.art-d65c8015a16b4d66a110fa4b50c0b3a52022-12-22T01:41:56ZengEuropean Survey Research AssociationSurvey Research Methods1864-33612012-12-016310.18148/srm/2012.v6i3.51305143Robust Lavallee-Hidiroglou stratified sampling strategyMaria Caterina Bramati0Sapienza University of RomeThere are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target variable Y, the objective of the survey, is unknown, some auxiliary information X known for the entire population from which the sample is drawn, is used. Such information is helpful as it is typically strongly correlated with the target Y. However, some discrepancies between these variables may arise. The use of auxiliary information, combined with the choice of the appropriate statistical model to estimate the relationship between Y and X, is crucial for the determination of the strata bounds, the size of the sample and the sampling rates according to a chosen precision level for the estimates, as has been shown by Rivest (2002). Nevertheless, this regression-based approach is highly sensitive to the presence of contaminated data. Since the key tool for stratified sampling is the measure of scale of Y conditional on the knowledge of the auxiliary X, a robust approach based on the S-estimator of the regression is proposed in this paper. The aim is to allow for robust sample size and strata bounds determination, together with optimal sample allocation. Simulation results based on data from the Construction sector of a Structural Business Survey illustrate the advantages of the proposed method.https://ojs.ub.uni-konstanz.de/srm/article/view/5130robust regressionstratified designauxiliary data |
| spellingShingle | Maria Caterina Bramati Robust Lavallee-Hidiroglou stratified sampling strategy Survey Research Methods robust regression stratified design auxiliary data |
| title | Robust Lavallee-Hidiroglou stratified sampling strategy |
| title_full | Robust Lavallee-Hidiroglou stratified sampling strategy |
| title_fullStr | Robust Lavallee-Hidiroglou stratified sampling strategy |
| title_full_unstemmed | Robust Lavallee-Hidiroglou stratified sampling strategy |
| title_short | Robust Lavallee-Hidiroglou stratified sampling strategy |
| title_sort | robust lavallee hidiroglou stratified sampling strategy |
| topic | robust regression stratified design auxiliary data |
| url | https://ojs.ub.uni-konstanz.de/srm/article/view/5130 |
| work_keys_str_mv | AT mariacaterinabramati robustlavalleehidirogloustratifiedsamplingstrategy |