Statistical Science


Multivariate and High Dimensional Data

Theme Overview

This theme has a research programme that encompasses both the theoretical and methodological problems encountered when analysing multivariate and high dimensional data. Much of the work in the area is driven by advances in technology in various application fields, where new forms of data with unprecedented levels of heterogeneity and complexity are in a modern setting collected routinely.

Current application problems that the group works on include medical imaging and near-infrared spectroscopy.

Theme members

Name Keywords
Jinghao Xue (Theme Lead) CVPR; dimension reduction; image analysis; sparse methods; subspace methods
Petros Dellaportas
Gaussian processes; sparsity
Tom Fearn
Bayesian methods; calibration; classification; multivariate analysis; near infrared spectroscopy
Simon Harden
Composite likelihoods
Christian Henning Classification; cluster analysis; data visualisation; market research; model selection; multivariate data analysis; robust statistics; social stratification; systematic biology
Sofia Olhede
Ecology; medical imaging; oceanography
Ricardo Silva
Causal inference; graphical models; latent variable models

Other members: Maria De Iorio, Serge Guillas, Patrick Wolfe.

Some of our current PhD students are also working on topics related to this theme.

Current and Recent Externally Funded Projects

  • High Dimensional Models for Multivariate Time Series Analysis, £990k, EPSRC EP/I005250/1, Oct 2010 - Sep 2015, PI: Olhede.