Abstract
In this paper, we consider the estimation and variable selection problem for partially linear additive spatial autoregressive models (PLASARM). We propose a robust estimation of two-stage Walsh-average regression (2SWAR) based on Walsh-average regression and instrumental variable method. Under some mild conditions, we obtain and theoretically prove the asymptotic normality of finite parameter vectors and the convergence rate of the nonparametric part. In addition, We also propose a robust variable selection method and further demonstrate its ability to consistently identify real models. We further carry out Monte Carlo simulation and real data analysis, both of which yield promising numerical results.
Funding Statement
The researches are supported by the Fundamental Research Funds for the Central Universities (No.23CX03012A), National Key Research and Development Program of China (2021YFA1000102).
Acknowledgments
The authors would like to thank the anonymous referees, an Associate Editor and the Editor for their constructive comments that improved the quality of this paper. The corresponding author: Yunquan Song, E-mail address: statistics99@163.com.
Citation
Zitong Li. Yunquan Song. "Two-stage Walsh-average-based robust estimation and variable selection for partially linear additive spatial autoregressive models." Braz. J. Probab. Stat. 37 (4) 667 - 692, December 2023. https://doi.org/10.1214/23-BJPS586
Information