Distributed methods for large scale regression

Lead Research Organisation: University of Bristol
Department Name: Mathematics

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509619/1 01/10/2016 30/09/2021
2064175 Studentship EP/N509619/1 16/04/2018 15/10/2021 Chibisi Chima-Okereke