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Titel Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling
VerfasserIn D. Das, J. Dy, J. Ross, Z. Obradovic, A. R. Ganguly
Medientyp Artikel
Sprache Englisch
ISSN 2198-5634
Digitales Dokument URL
Erschienen In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-04-11), S.615-648
Datensatznummer 250115087
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/npgd-1-615-2014.pdf
 
Zusammenfassung
Climate projections simulated by Global Climate Models (GCM) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often precludes their application towards accurately assessing the effects of climate change on finer regional scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods are often performed for regional climate projections. Statistical downscaling (SD) is based on the understanding that the regional climate is influenced by two factors – the large scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model which relates these features (predictors) to a climatic variable of interest (predictand) based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP), for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence relatively more generalizable than non-sparse alternatives, and lends to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling shows our method can lead to new insights.
 
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