Spatial lasso with applications to GIS model selection

Journal of Computational and Graphical Statistics

Volumn 19, Issue 4, 2010, Pages 963-983

  Huang, Hsin Cheng ^a   Hsu, Nan Jung ^b   Theobald, David M ^c   Breidt, F Jay ^c  

^a INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^c Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Cross validation; Generalized least squares; Least angle regression; Spatial regression; Variable selection

Indexed keywords

EID: 77954095581     PISSN: 10618600     EISSN: None     Source Type: Journal    
DOI: 10.1198/jcgs.2010.07102     Document Type: Article

References (18)

1. Akaike, H. (1973), "Information Theory and the Maximum Likelihood Principle," in International Symposium on Information Theory, eds. V Petrov and F. Csáki, Budapest: Akademiai Kiádo, pp. 267-281. [966]
2. Breiman, L. (1995), "Better Subset Regression Using the Nonnegative Garrote," Technometrics, 37, 373-384. [981]
3. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), "Least Angle Regression" (with discussion), The Annals ofStatistics, 32, 407-499. [964,967,968]
4. Fan, J., and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties," Journal of the American Statistical Association, 96, 1348-1360. [981] (Pubitemid 33695585)
5. Fingleton, B. (1986), "Analyzing Cross-Classified Data With Inherent Spatial Dependence," Geographic Analy-sis, 18, 48-61. [979]
6. Gerrard, D. J. (1969), "Competition Quotient: A New Measure of the Competition Affecting Individual Forest Trees," Research Bulletin 20, Agricultural Experiment Station, Michigan State University. [979]
7. Iverson, L. R., Dale, M. E., Scott, C T., and Prasad, A. (1996), "A GIS-Derived Integrated Moisture Index to Predict Forest Composition and Productivity of Ohio Forests (U.SA.)," Landscape Ecology, 12, 331-348. [975] (Pubitemid 127507162)
8. Lin, P-S. (2008), "Estimating Equations for Spatially Correlated Data in Multi-Dimensional Space," Biometrika, 95, 847-858. [979]
9. Osborne, M., Presnell, B., and Turlach, B. (2000a), "A New Approach to Variable Selection in Least Squares Problems," IMA Journal of Numerical Analysis, 20, 389-403. [967]
10. (2000b), "On the LASSO and Its Dual," Journal of Computational and Graphical Statistics, 9, 319-337. [967]
11. Tibshirani, R. (1996), "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society, Ser B, 58, 267-288. [964,966-968]
12. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005), "Sparsity and Smoothness via the Fused Lasso," Journal of the Royal Statistical Society, Ser B, 67, 91-108. [981] (Pubitemid 40167043)
13. 1 Con-straint," in 2004 Proceedings of the Statistical Computing Section, Alexandria, VA: American Statistical Association, pp. 2572-2577. [968]
14. Turlach, B. A., Venables, W. N., and Wright, S. J. (2005), "Simultaneous Variable Selection," Technometrics, 47, 349-363. [981] (Pubitemid 41164070)
15. Ye, J. (1998), "On Measuring and Correcting the Effects of Data Mining and Model Selection," Journal of the American Statistical Association, 93, 120-131. [967]
16. Yuan, M., and Lin, Y (2006), "Model Selection and Estimation in Regression With Grouped Variables," Journal of the Royal Statistical Society, Ser B, 68, 49-67. [981] (Pubitemid 43415335)
17. Zou, H., and Hastie, T. (2005), "Regularization and Variable Selection via the Elastic Net," Journal of the Royal Statistical Society, Ser. B, 67, 301-320. [981] (Pubitemid 40465877)
18. Zou, H., Hastie, T., and Tibshirani, R. (2007), "On the 'Degrees of Freedom' of the Lasso," The Annals of Statistics, 35, 2173-2192. [967]