Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach

By Michael Tiefelsdorf, Daniel A. Griffith. 2007

328 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Health

View Paper: https://www.semanticscholar.org/paper/23775da84d4e489c46957e203320c599329f489c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. B. Hawkins
2. J. Diniz-Filho
3. Luis Mauricio Bini
4. Stéphane Dray
5. Zsuzsanna Csereklyei
6. Luc Anselin
7. Yongwan Chun
8. Y. Yamagata

In Health

1. Luc Anselin
2. David I. Stern
3. Zsuzsanna Csereklyei
4. Yongwan Chun
5. Stéphane Dray
6. D. Bauman
7. Thomas Drouet
8. J. Vleminckx

Citations in the corpus

• Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors (2018, methodology): “The third most widely used variable selection method consists of selecting the smallest subset of predictors that best minimises the spatial autocorrelation in model residuals (Griffith and Peres-Neto 2006, Tiefelsdorf and Griffith 2007).”
• Modeling Network Autocorrelation in Space–Time Migration Flow Data: An Eigenvector Spatial Filtering Approach (2011, methodology): “Griffith (2000), extended by Tiefelsdorf and Griffith (2007), described how a spatial filter, which is a linear combination of selected eigenvectors, can be constructed in a stepwise manner, can account for spatial components, and results in unbiased parameter estimates.”
• Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology (2007, background): “It is important to note that we do not present a formal evaluation of this issue using statistical theory (see Cressie 1993, Schabenberg and Gotway 2005, Tiefelsdorf and Griffith 2007).”
• The Gravity Model for International Trade: Specification and Estimation Issues (2015, unknown): “Following Tiefelsdorf & Griffith (2007) we identified Q = 52 eigenvectors.”
• Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR) (2008, background): “Eigenfunction-based spatial filter construction is discussed by Tiefelsdorf and Griffith (2007), among others.”
• Spatial Structure and Spatial Interaction: 25 Years Later (2007, methodology): “can be based upon the conventional residual sum-of-squares-minimization criterion or, as proposed by Tiefelsdorf and Griffith (2007), a residual MC-minimization criterion.”
• Global Energy Use: Decoupling or Convergence? (2015, methodology): “While Tiefelsdorf and Griffith (2007) set the threshold at 0.1, they note that for smaller panels, with less than 50 spatial observations δ = 1 could be used.”
• A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “5 λl /λ1 > 0.25 and λl > 0 are commonly used criteria (e.g., Griffith, 2003; Tiefelsdorf and Griffith, 2007; Drey, 2006; Hughes and Haran, 2013).”
• Thirty years of spatial econometrics (2010, methodology): “Spatial filtering gains some attention as an approach to remove spatial correlation from variables in a range of models, including models for counts, e.g., in Getis and Griffith (2002); Griffith (2003); Griffith and Peres-Neto (2006); and Tiefelsdorf and Griffith (2007).”