Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/20148
Title: Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study
Contributor(s): Fitzpatrick, Benjamin R (author); Lamb, David  (author); Mengersen, Kerrie (author)
Publication Date: 2016
Open Access: Yes
DOI: 10.1371/journal.pone.0162489Open Access Link
Handle Link: https://hdl.handle.net/1959.11/20148
Abstract: Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment.
Publication Type: Journal Article
Source of Publication: PLoS One, 11(9), p. 1-19
Publisher: Public Library of Science (PLoS)
Place of Publication: United States of America
ISSN: 1932-6203
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
010401 Applied Statistics
Socio-Economic Outcome Codes: 961402 Farmland, Arable Cropland and Permanent Cropland Soils
961403 Forest and Woodlands Soils
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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