We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Robust variable selection with exponential squared loss for the partially linear varying coefficient spatial autoregressive model.
- Authors
Yu, Jialei; Song, Yunquan; Du, Jiang
- 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.
- Subjects
AIRBNB Inc.; NONPARAMETRIC estimation; PARAMETER estimation; AUTOREGRESSIVE models; VECTOR valued functions; COMPUTER simulation
- Publication
Environmental & Ecological Statistics, 2024, Vol 31, Issue 1, p97
- ISSN
1352-8505
- Publication type
Article
- DOI
10.1007/s10651-024-00603-z