Sparse Group Lasso for regression on land climate variables

Soumyadeep Chatterjee; Arindam Banerjee; Singdhansu B Chatterjee; Auroop R. Ganguly

doi:10.1109/ICDMW.2011.155

Sparse Group Lasso for regression on land climate variables

Soumyadeep Chatterjee, Arindam Banerjee, Singdhansu B Chatterjee, Auroop R. Ganguly

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages	1-8
Number of pages	8
DOIs	https://doi.org/10.1109/ICDMW.2011.155
State	Published - Dec 1 2011
Event	11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada Duration: Dec 11 2011 → Dec 11 2011

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Country/Territory	Canada
City	Vancouver, BC
Period	12/11/11 → 12/11/11

Keywords

Climate prediction
Sparse Group Lasso
Sparse regression

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICDMW.2011.155

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Sparse Group Lasso for regression on land climate variables. / Chatterjee, Soumyadeep; Banerjee, Arindam ; Chatterjee, Singdhansu B et al.

Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011. 2011. p. 1-8 6137353 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chatterjee, S, Banerjee, A , Chatterjee, SB & Ganguly, AR 2011, Sparse Group Lasso for regression on land climate variables. in Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011., 6137353, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1-8, 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011, Vancouver, BC, Canada, 12/11/11. https://doi.org/10.1109/ICDMW.2011.155

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title = "Sparse Group Lasso for regression on land climate variables",

abstract = "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.",

keywords = "Climate prediction, Sparse Group Lasso, Sparse regression",

author = "Soumyadeep Chatterjee and Arindam Banerjee and Chatterjee, {Singdhansu B} and Ganguly, {Auroop R.}",

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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

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ER -

Sparse Group Lasso for regression on land climate variables

Abstract

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Other

Keywords

UN SDGs

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