Dr Stuart Barber
- Co-Head of Faculty Graduate School
My main current area of interest is the application of wavelet methods in statistics. Wavelets are a class of functions which can be used to generate orthogonal bases for spaces of functions. Because of the way wavelets are designed, the description of a 'nice' function in terms of a wavelet basis tends to be very efficient. Here, 'efficient' means that you only need a few wavelets to describe quite complicated functions.
Wavelets have been used in engineering, signal processing, and numerical analysis for some time. In the last fifteen years the statistical community has been applying wavelets to problems including nonparametric regression, density estimation, time series analysis, and changepoint detection. Recent applications in my own work has been to use wavelets and related methods in areas as diverse as clustering, phylogenetics, industrial tomography, anaesthesiology, spatial data analysis, and pseudo-random number generation algorithms.
Research groups and institutes
<li><a href="//phd.leeds.ac.uk/project/239-locally-stationary-wavelet-process-models-for-autoregressive-conditional-duration-data">Locally stationary wavelet process models for autoregressive conditional duration data</a></li>
<li><a href="//phd.leeds.ac.uk/project/248-multiscale-statistical-classification">Multiscale statistical classification</a></li>