Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters |
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Authors: | Yongwan Chun Daniel A Griffith Monghyeon Lee Parmanand Sinha |
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Institution: | 1.School of Economic, Political and Policy Sciences,The University of Texas at Dallas,Richardson,USA;2.Department of Geography,The University of Tennessee,Knoxville,USA |
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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. |
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