Robust variable selection with exponential squared loss for partially linear spatial autoregressive models

Xiuli Wang (), Jingchang Shao (), Jingjing Wu () and Qiang Zhao ()
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Xiuli Wang: Shandong Normal University
Jingchang Shao: Shandong Normal University
Jingjing Wu: University of Calgary
Qiang Zhao: Shandong Normal University

Annals of the Institute of Statistical Mathematics, 2023, vol. 75, issue 6, No 4, 949-977

Abstract: 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.

Keywords: Orthogonal projection; Exponential squared loss; Semiparametric spatial autoregressive models; Oracle property; Variable selection (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10463-023-00870-w

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