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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
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| 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
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| Publikation (Nr.) |
 copernicus.org/npgd-1-615-2014.pdf |
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| 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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