High-Dimensional Big Data Modelling with Applications to Child Health and Forensic Science

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This project will build on recent work that developed models that provide clinicians with a better understand of changes in children’s eyes as they get older. The models for visual acuity measurements made on each eye were based on two-dimensional probability density functions called copulas and smooth curves called splines that relate the shape of the copula density to the covariate age. The challenges that will be addressed by this project are the extension of the modelling methodology from two to many dimensions and from one to many covariates. The main tool for addressing this problem will be a very flexible way of generating high-dimensional probability density functions based on a tree-based representation called a vine. These models will help us to understand how the dependencies between many variables change with other information. In particular, multiple testing of sick children can lead to higher than necessary referral rates, which our methodology will reduce by taking proper account of the dependencies between tests, in the light of other diagnostic information. This project will also work with data from forensic science where the aim will be to understand how measurements of long human bone structures depend on measurements taken from the skull.

Supervisor: Dr Luciana Dalla Valle
Second supervisor: Dr Julian Stander

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