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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/5/757 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2227-7390 |