Spatial kernel regression estimation: weak consistency.
In this paper, we introduce a kernel method to estimate a spatial conditional regression under mixing spatial processes. Some preliminary statistical properties including weak consistency and convergence rates are investigated. The sufficient conditions on mixing coefficients and the bandwidth are e...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Elsevier
2004
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| Online Access: | http://hdl.handle.net/20.500.11937/13839 |
| _version_ | 1848748454081200128 |
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| author | Lu, Zudi Chen, X. |
| author_facet | Lu, Zudi Chen, X. |
| author_sort | Lu, Zudi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we introduce a kernel method to estimate a spatial conditional regression under mixing spatial processes. Some preliminary statistical properties including weak consistency and convergence rates are investigated. The sufficient conditions on mixing coefficients and the bandwidth are established to ensure distribution-free weak consistency, which requires no assumption on the regressor and allows the mixing coefficients decreasing to zero slowly. However, to achieve an optimal convergence rate, some requirements on the regressor and the decreasing rate of mixing coefficients tending to zero are needed. |
| first_indexed | 2025-11-14T07:05:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-13839 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:05:18Z |
| publishDate | 2004 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-138392017-09-13T15:54:09Z Spatial kernel regression estimation: weak consistency. Lu, Zudi Chen, X. Kernel estimator Mixing spatial processes Weak consistency and rates Spatial regression Bandwidth In this paper, we introduce a kernel method to estimate a spatial conditional regression under mixing spatial processes. Some preliminary statistical properties including weak consistency and convergence rates are investigated. The sufficient conditions on mixing coefficients and the bandwidth are established to ensure distribution-free weak consistency, which requires no assumption on the regressor and allows the mixing coefficients decreasing to zero slowly. However, to achieve an optimal convergence rate, some requirements on the regressor and the decreasing rate of mixing coefficients tending to zero are needed. 2004 Journal Article http://hdl.handle.net/20.500.11937/13839 10.1016/j.spl.2003.08.014 Elsevier restricted |
| spellingShingle | Kernel estimator Mixing spatial processes Weak consistency and rates Spatial regression Bandwidth Lu, Zudi Chen, X. Spatial kernel regression estimation: weak consistency. |
| title | Spatial kernel regression estimation: weak consistency. |
| title_full | Spatial kernel regression estimation: weak consistency. |
| title_fullStr | Spatial kernel regression estimation: weak consistency. |
| title_full_unstemmed | Spatial kernel regression estimation: weak consistency. |
| title_short | Spatial kernel regression estimation: weak consistency. |
| title_sort | spatial kernel regression estimation: weak consistency. |
| topic | Kernel estimator Mixing spatial processes Weak consistency and rates Spatial regression Bandwidth |
| url | http://hdl.handle.net/20.500.11937/13839 |