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Variable Selection of Heterogeneous Spatial Autoregressive Models via Double-Penalized Likelihood
| Content Provider | MDPI |
|---|---|
| Author | Tian, Ruiqin Xia, Miaojie Xu, Dengke |
| Copyright Year | 2022 |
| Description | 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 estimation and the variable selection for both the mean and variance models. Then, a unified procedure via double-penalized quasi-maximum likelihood is proposed, to simultaneously select important variables. Under certain regular conditions, the consistency and oracle property of the resulting estimators are established. Finally, both simulation studies and a real data analysis of the Boston housing data are carried to illustrate the developed methodology. |
| Starting Page | 1200 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym14061200 |
| Journal | Symmetry |
| Issue Number | 6 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-10 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Mathematical Social Sciences Heterogeneous Spatial Autoregressive Models Double-penalized Quasi-maximum Likelihood Variable Selection Scad Tuning Parameters |
| Content Type | Text |
| Resource Type | Article |
