TY - GEN
T1 - Sparse Group Lasso for regression on land climate variables
AU - Chatterjee, Soumyadeep
AU - Banerjee, Arindam
AU - Chatterjee, Snigdhansu
AU - Ganguly, Auroop R.
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - The large amount of reliable climate data available today has promoted the development of statistical predictive models for climate variables. In this paper we have applied Sparse Group Lasso to build a predictive model for land climate variables using ocean climate variables as covariates.We demonstrate that the sparse model provides better predictive performance than the state-of-the-art, is climatologically interpretable and robust in variable selection.
AB - The large amount of reliable climate data available today has promoted the development of statistical predictive models for climate variables. In this paper we have applied Sparse Group Lasso to build a predictive model for land climate variables using ocean climate variables as covariates.We demonstrate that the sparse model provides better predictive performance than the state-of-the-art, is climatologically interpretable and robust in variable selection.
KW - Climate prediction
KW - Sparse Group Lasso
KW - Sparse regression
UR - http://www.scopus.com/inward/record.url?scp=84857153350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857153350&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2011.155
DO - 10.1109/ICDMW.2011.155
M3 - Conference contribution
AN - SCOPUS:84857153350
SN - 9780769544090
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1
EP - 8
BT - Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
T2 - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Y2 - 11 December 2011 through 11 December 2011
ER -