1. Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters.
- Author
-
Chun, Yongwan, Griffith, Daniel, Lee, Monghyeon, and Sinha, Parmanand
- Subjects
- *
EIGENVECTORS , *REGRESSION analysis , *SPATIAL filters , *AUTOCORRELATION (Statistics) , *DATA extraction , *NONLINEAR equations - Abstract
Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF