Variable Selection of Heterogeneous Spatial Autoregressive Models via Double-Penalized Likelihood

Variable Selection of Heterogeneous Spatial Autoregressive Models via Double-Penalized Likelihood

Heteroscedasticity is often encountered in spatial-data analysis, so a new class of heterogeneous spatial autoregressive models is introduced in this paper, where the variance parameters are allowed to depend on some explanatory variables. Here, we are interested in the problem of parameter estimati...

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Bibliographic Details
Main Authors: Ruiqin Tian, Miaojie Xia, Dengke Xu
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Symmetry
Subjects:
heterogeneous spatial autoregressive models
double-penalized quasi-maximum likelihood
variable selection
SCAD
tuning parameters
Online Access:https://www.mdpi.com/2073-8994/14/6/1200

Internet

https://www.mdpi.com/2073-8994/14/6/1200

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