Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling
Climate projections simulated by Global Climate Models (GCMs) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often preclude their application to accurately assessing the effects of climate change on finer regional-scale phenomena...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-12-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/21/1145/2014/npg-21-1145-2014.pdf |
| Summary: | Climate projections simulated by Global Climate Models (GCMs) are often used
for assessing the impacts of climate change. However, the relatively coarse
resolutions of GCM outputs often preclude their application to
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 is 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 that 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
more generalizable than non-sparse alternatives, and lend themselves 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 show that our method can lead to new insights. |
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| ISSN: | 1023-5809 1607-7946 |