Dimension reduction in spatial regression with kernel SAVE method
We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Académie des sciences
2021-06-01
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| Series: | Comptes Rendus. Mathématique |
| Online Access: | https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.187/ |
| _version_ | 1797651564240830464 |
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| author | Affossogbe, Mètolidji Moquilas Raymond Nkiet, Guy Martial Ogouyandjou, Carlos |
| author_facet | Affossogbe, Mètolidji Moquilas Raymond Nkiet, Guy Martial Ogouyandjou, Carlos |
| author_sort | Affossogbe, Mètolidji Moquilas Raymond |
| collection | DOAJ |
| description | We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR) space, and show their consistency. |
| first_indexed | 2024-03-11T16:16:36Z |
| format | Article |
| id | doaj.art-c1d284fe2fdc4c3aa84c75bd8ea07d7f |
| institution | Directory Open Access Journal |
| issn | 1778-3569 |
| language | English |
| last_indexed | 2024-03-11T16:16:36Z |
| publishDate | 2021-06-01 |
| publisher | Académie des sciences |
| record_format | Article |
| series | Comptes Rendus. Mathématique |
| spelling | doaj.art-c1d284fe2fdc4c3aa84c75bd8ea07d7f2023-10-24T14:19:16ZengAcadémie des sciencesComptes Rendus. Mathématique1778-35692021-06-01359447547910.5802/crmath.18710.5802/crmath.187Dimension reduction in spatial regression with kernel SAVE methodAffossogbe, Mètolidji Moquilas Raymond0Nkiet, Guy Martial1Ogouyandjou, Carlos2Institut de Mathématiques et de Sciences Physiques,Porto Novo, BéninUniversité des Sciences et Techniques de Masuku, Franceville, GabonInstitut de Mathématiques et de Sciences Physiques,Porto Novo, BéninWe consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR) space, and show their consistency.https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.187/ |
| spellingShingle | Affossogbe, Mètolidji Moquilas Raymond Nkiet, Guy Martial Ogouyandjou, Carlos Dimension reduction in spatial regression with kernel SAVE method Comptes Rendus. Mathématique |
| title | Dimension reduction in spatial regression with kernel SAVE method |
| title_full | Dimension reduction in spatial regression with kernel SAVE method |
| title_fullStr | Dimension reduction in spatial regression with kernel SAVE method |
| title_full_unstemmed | Dimension reduction in spatial regression with kernel SAVE method |
| title_short | Dimension reduction in spatial regression with kernel SAVE method |
| title_sort | dimension reduction in spatial regression with kernel save method |
| url | https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.187/ |
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