Robust Variable Selection with Exponential Squared Loss for the Spatial Single-Index Varying-Coefficient Model

Autor:	Yezi Wang, Zhijian Wang, Yunquan Song
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	spatial single-index varying-coefficient model exponential squared loss variable selection Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 25, Iss 2, p 230 (2023)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e25020230
Popis:	As spatial correlation and heterogeneity often coincide in the data, we propose a spatial single-index varying-coefficient model. For the model, in this paper, a robust variable selection method based on spline estimation and exponential squared loss is offered to estimate parameters and identify significant variables. We establish the theoretical properties under some regularity conditions. A block coordinate descent (BCD) algorithm with the concave–convex process (CCCP) is composed uniquely for solving algorithms. Simulations show that our methods perform well even though observations are noisy or the estimated spatial mass matrix is inaccurate.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/a32d78baf4fd46d989597778f445ae0d Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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