论文信息 - Living with Collinearity in Local Regression Models

Living with Collinearity in Local Regression Models

We investigate the issue of collinearity in data when using Geographically Weighted Regression to explore spatial variation in data sets – and show how the ideas of condition numbers and variance inflation factors may be `localised’ to detect and respond to problems caused by this phenomenon.

[1] W. W. Muir,et al. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .

[2] Edzer J. Pebesma,et al. Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[3] David C. Wheeler,et al. Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso , 2009 .

[4] J. Friedman,et al. A Statistical View of Some Chemometrics Regression Tools , 1993 .

[5] J. Hair. Multivariate data analysis , 1972 .

[6] R. O’Brien,et al. A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[7] A. Stewart Fotheringham,et al. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[8] Pierre Goovaerts,et al. Geostatistical modelling of uncertainty in soil science , 2001 .

[9] R. H. Myers. Classical and modern regression with applications , 1986 .

[10] D. Wheeler. Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression , 2007 .

[11] David Wheeler,et al. Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..