Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality
Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/5/757 |
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| author | Mu Yue Jingxin Xi |
| author_facet | Mu Yue Jingxin Xi |
| author_sort | Mu Yue |
| collection | DOAJ |
| description | Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive numerical examples illustrate the advantage of the proposed methodology. An application of Boston housing price data is further provided to demonstrate the proposed methodology. |
| format | Article |
| id | doaj-art-88cb97d0418a4a01b45d0babd01fe5ac |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-88cb97d0418a4a01b45d0babd01fe5ac2025-08-20T02:59:15ZengMDPI AGMathematics2227-73902025-02-0113575710.3390/math13050757Sparse Boosting for Additive Spatial Autoregressive Model with High DimensionalityMu Yue0Jingxin Xi1School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798, SingaporeSchool of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798, SingaporeVariable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive numerical examples illustrate the advantage of the proposed methodology. An application of Boston housing price data is further provided to demonstrate the proposed methodology.https://www.mdpi.com/2227-7390/13/5/757sparse boostingvariable selectionspatial autoregressive modeladditive modelinstrument variable |
| spellingShingle | Mu Yue Jingxin Xi Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality Mathematics sparse boosting variable selection spatial autoregressive model additive model instrument variable |
| title | Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality |
| title_full | Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality |
| title_fullStr | Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality |
| title_full_unstemmed | Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality |
| title_short | Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality |
| title_sort | sparse boosting for additive spatial autoregressive model with high dimensionality |
| topic | sparse boosting variable selection spatial autoregressive model additive model instrument variable |
| url | https://www.mdpi.com/2227-7390/13/5/757 |
| work_keys_str_mv | AT muyue sparseboostingforadditivespatialautoregressivemodelwithhighdimensionality AT jingxinxi sparseboostingforadditivespatialautoregressivemodelwithhighdimensionality |