Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model
This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. With approximating to the functiona...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/1616068 |
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| _version_ | 1850209908614496256 |
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| author | Dengke Xu Ruiqin Tian Ying Lu |
| author_facet | Dengke Xu Ruiqin Tian Ying Lu |
| author_sort | Dengke Xu |
| collection | DOAJ |
| description | This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. With approximating to the functional coefficient by Karhunen–Loève representation, we propose a Bayesian adaptive Lasso method to simultaneously estimate unknown parameters and select important covariates in the model, which can be performed by combining the Gibbs sampler and the Metropolis–Hastings algorithm. Some simulation studies are conducted and the results show that the proposed Bayesian method behaves well. |
| format | Article |
| id | doaj-art-9212f23b18ed49f98bf986934bf379ee |
| institution | OA Journals |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-9212f23b18ed49f98bf986934bf379ee2025-08-20T02:09:55ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/1616068Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive ModelDengke Xu0Ruiqin Tian1Ying Lu2School of EconomicsSchool of MathematicsSchool of Data Science and Media IntelligenceThis study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. With approximating to the functional coefficient by Karhunen–Loève representation, we propose a Bayesian adaptive Lasso method to simultaneously estimate unknown parameters and select important covariates in the model, which can be performed by combining the Gibbs sampler and the Metropolis–Hastings algorithm. Some simulation studies are conducted and the results show that the proposed Bayesian method behaves well.http://dx.doi.org/10.1155/2022/1616068 |
| spellingShingle | Dengke Xu Ruiqin Tian Ying Lu Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model Journal of Mathematics |
| title | Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model |
| title_full | Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model |
| title_fullStr | Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model |
| title_full_unstemmed | Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model |
| title_short | Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model |
| title_sort | bayesian adaptive lasso for the partial functional linear spatial autoregressive model |
| url | http://dx.doi.org/10.1155/2022/1616068 |
| work_keys_str_mv | AT dengkexu bayesianadaptivelassoforthepartialfunctionallinearspatialautoregressivemodel AT ruiqintian bayesianadaptivelassoforthepartialfunctionallinearspatialautoregressivemodel AT yinglu bayesianadaptivelassoforthepartialfunctionallinearspatialautoregressivemodel |