Distributed methods for large scale regression
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Distributed methods for large scale regression
CoPED ID
40d4cadf-3e40-42b2-921f-d09bcff2ef49
Status
Closed
Value
No funds listed.
Start Date
April 15, 2018
End Date
Dec. 4, 2018
Description
Automatically collected data in environmental modelling, energy management and medicine may involve very large data volumes while also requiring richly parameterized models for adequate analysis and prediction. If n is data set size and p the number of model coefficients, this project aims to find O(np) computational methods for estimating penalized regression models, which are susceptible to parallelization in cluster computing environments. The major challenge is to do this in a way that adequately estimates hyper-parameters alongside regression coefficients, and the project will investigate the feasibility of doing this using stochastic log determinant or log trace estimators in the context of marginal likelihood or similar criteria.
| University of Bristol | LEAD_ORG |
| Simon Wood | SUPER_PER |
Subjects by relevance
- Energy management
- Forecasts
- Machine learning
- Estimating (statistical methods)
Extracted key phrases
- Large datum volume
- Large scale regression
- Regression model
- Regression coefficient
- Model coefficient
- Computational method
- Environmental modelling
- Energy management
- Stochastic log determinant
- Cluster computing environment
- Adequate analysis
- Project
- Medicine
- Major challenge