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Robust variable selection with exponential squared loss for the partially linear varying coefficient spatial autoregressive model.
- Source :
- Environmental & Ecological Statistics; Mar2024, Vol. 31 Issue 1, p97-127, 31p
- Publication Year :
- 2024
-
Abstract
- The partially linear varying coefficient spatial autoregressive model is a semi-parametric spatial autoregressive model in which the coefficients of some explanatory variables are variable, while the coefficients of the remaining explanatory variables are constant. For the nonparametric part, a local linear smoothing method is used to estimate the vector of coefficient functions in the model, and, to investigate its variable selection problem, this paper proposes a penalized robust regression estimation based on exponential squared loss, which can estimate the parameters while selecting important explanatory variables. A unique solution algorithm is composed using the block coordinate descent (BCD) algorithm and the concave-convex process (CCCP). Robustness of the proposed variable selection method is demonstrated by numerical simulations and illustrated by some housing data from Airbnb. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13528505
- Volume :
- 31
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Environmental & Ecological Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 176083223
- Full Text :
- https://doi.org/10.1007/s10651-024-00603-z