On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment
Small area models have become popular methods for producing reliable estimates for sub-populations (small geographic areas in this study). Small area modeling may be carried out via model-assisted approaches within the model-based approaches or design-based paradigm. When there are medium or large s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2022/3865626 |
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| _version_ | 1849404213693513728 |
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| author | Kindie Fentahun Muchie Anthony Kibira Wanjoya Samuel Musili Mwalili |
| author_facet | Kindie Fentahun Muchie Anthony Kibira Wanjoya Samuel Musili Mwalili |
| author_sort | Kindie Fentahun Muchie |
| collection | DOAJ |
| description | Small area models have become popular methods for producing reliable estimates for sub-populations (small geographic areas in this study). Small area modeling may be carried out via model-assisted approaches within the model-based approaches or design-based paradigm. When there are medium or large samples, a model-assisted approach may be reliable. However, when data are scarce, a model-based technique may be required. Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. Nevertheless, things become more complex when the geographic boundaries of interest are misaligned. Some authors have addressed the problem of misalignment under hierarchical Bayesian approach. In this study, we developed non-trivial extension of existing hierarchical Bayesian model for a binary outcome variable under spatial misalignment with three contributions. First, the model uses unit-level survey data and area-level auxiliary data to predict the posterior mean proportion spatially at the second geographic area level. Second, the linking model is changed to logit-normal model in the proposed model. Lastly, the mean process was considered to overcome the multicollinearity between the true predictors and the spatial random effect. Sensitivity analysis was also done via simulation. |
| format | Article |
| id | doaj-art-9960a5115bd84da49c6d571cca4e697c |
| institution | Kabale University |
| issn | 1687-9538 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-9960a5115bd84da49c6d571cca4e697c2025-08-20T03:37:03ZengWileyJournal of Probability and Statistics1687-95382022-01-01202210.1155/2022/3865626On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial MisalignmentKindie Fentahun Muchie0Anthony Kibira Wanjoya1Samuel Musili Mwalili2Pan African University Institute for Basic SciencesJomo Kenyatta University of Agriculture and TechnologyJomo Kenyatta University of Agriculture and TechnologySmall area models have become popular methods for producing reliable estimates for sub-populations (small geographic areas in this study). Small area modeling may be carried out via model-assisted approaches within the model-based approaches or design-based paradigm. When there are medium or large samples, a model-assisted approach may be reliable. However, when data are scarce, a model-based technique may be required. Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. Nevertheless, things become more complex when the geographic boundaries of interest are misaligned. Some authors have addressed the problem of misalignment under hierarchical Bayesian approach. In this study, we developed non-trivial extension of existing hierarchical Bayesian model for a binary outcome variable under spatial misalignment with three contributions. First, the model uses unit-level survey data and area-level auxiliary data to predict the posterior mean proportion spatially at the second geographic area level. Second, the linking model is changed to logit-normal model in the proposed model. Lastly, the mean process was considered to overcome the multicollinearity between the true predictors and the spatial random effect. Sensitivity analysis was also done via simulation.http://dx.doi.org/10.1155/2022/3865626 |
| spellingShingle | Kindie Fentahun Muchie Anthony Kibira Wanjoya Samuel Musili Mwalili On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment Journal of Probability and Statistics |
| title | On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment |
| title_full | On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment |
| title_fullStr | On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment |
| title_full_unstemmed | On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment |
| title_short | On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment |
| title_sort | on hierarchical bayesian spatial small area model for binary data under spatial misalignment |
| url | http://dx.doi.org/10.1155/2022/3865626 |
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