Computational Statistics

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As we move further into the data science era, the data types being produced are becoming increasingly complex. Paul McNicholas, Canada Research Chair in Computational Statistics, is developing new, computationally intensive statistical methods to gain better insight into several increasingly complex data types. In particular, work during the second term of the Chair will focus on three objectives. First of all, an approach will be developed to effectively handle outliers in three-way data, e.g., black and white images or multivariable longitudinal data. Then, work on tensor-variate distributions will be completed so that, e.g., coloured images can be effectively clustered. Finally, all of the aforementioned approaches will be implemented and made freely available as open source software. As part of the implementation process, the use of alternative algorithms will be investigated so that the most practically useful software is ultimately released.