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- Publisher
- Springer Journals
- Copyright
- Copyright © The Institute of Statistical Mathematics, Tokyo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
- ISSN
- 0020-3157
- eISSN
- 1572-9052
- DOI
- 10.1007/s10463-023-00870-w
- Publisher site
- See Article on Publisher Site
Abstract
In this paper, we consider variable selection for a class of semiparametric spatial autoregressive models based on exponential squared loss (ESL). Using the orthogonal projection technique, we propose a novel orthogonality-based variable selection procedure that enables simultaneous model selection and parameter estimation, and identifies the significance of spatial effects. Under appropriate conditions, we show that the proposed procedure is consistent and the resulting estimator has oracle properties. Furthermore, some simulation studies and an analysis of the Boston housing price data are also carried out to examine the finite-sample performance of the proposed method.
Journal
Annals of the Institute of Statistical Mathematics – Springer Journals
Published: Dec 1, 2023
Keywords: Orthogonal projection; Exponential squared loss; Semiparametric spatial autoregressive models; Oracle property; Variable selection