Professor in the Department of Mathematics and Statistics at York University.
- My main research interests are in nonparametric and semi-parametric statistics.
- A considerable amount of my recent work is in shape-constrained maximum likelihood estimation. The key idea behind this approach is to strike a balance between parametric methods and purely nonparametric methods by specifying a shape (e.g. convexity) of the function of interest. Unlike kernel smoothing methods, shape-constrained methodology does not require a choice of bandwidth, as it is automatically locally adaptive.
- I am also interested in statistical inference for random sets. Random sets are a natural approach to the analysis of shapes, and do not require user-defined landmarks, making them more natural in certain contexts. A closely associated work is the Bayesian approach to principal curves, which shows an application to the analysis of SPECT medical imaging data.
- My interest in random sets has recently led to research in effective dose estimation in the multivariate setting. When more than one agent is present, the effective dose estimator is essentially a random set. Thus far, my research has focused on developing practical confidence regions in the multivariate setting.
- I am also interested in applied and collaborative work. Some recent/current projects include flightpath recovery for migrating songbirds and clustering of the H3N2 flu virus.
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