Sparse Group Lasso: Consistency and Climate Applications
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The design of statistical predictive models for climate data gives rise to some unique challenges due to the high dimensionality and spatio-temporal nature of the datasets, which dictate that models should exhibit parsimony in variable selection. Recently, a class of methods which promote structured sparsity in the model have been developed, which is suitable for this task. In this paper, we prove theoretical statistical consistency of estimators with tree-structured norm regularizers. We consider one particular model, the Sparse Group Lasso (SGL), to construct predictors of land climate using ocean climate variables. Our experimental results demonstrate that the SGL model provides better predictive performance than the current state-of-the-art, remains climatologically interpretable, and is robust in its variable selection.
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In recent work, using sparse group Lasso to model climate variables ensured that if a location is selected, all climate variables observed at that location are used as relevant features.
“By treating the set of variables observed at every location as a group, the use of group Lasso ensured that if a location is selected, all of the climate variables observed at that location will be used as relevant features.”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [66]
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- Authors
- Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., & Ganguly, A.
- Affiliations
- University of Minnesota (US)
- Northeastern University (US)
- ORCID
- Venue
- Proceedings of the 2012 SIAM International Conference on Data Mining
- Published
- 2012-04-26
- Pages
- 47-58
- OpenAlex
- W2400197280
- Cited by
- 72
- References
- 30
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 7 · 2019: 5 · 2018: 6 · 2017: 10 · 2016: 6 · 2015: 10 · 2014: 6 · 2013: 2 · 2012: 3
- FWCI
- 16.93
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- SIAM International Conference on Data Mining (publishedVersion)
- Funders
- National Science Foundation (0851705, 0916750, CAREER, 1029711, 0953274, 0812183)
- University of Minnesota
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- sparse group lasso, structured sparsity, statistical consistency, feature selection, climate modeling
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