Past Seminar Series

Date: 2011-09-16

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Pair-copula constructions (PCCs) provide an elegant way to construct highly flexible multivariate distributions. However, for convenience of inference, pair-copulas are often assumed to depend on the conditioning variables only indirectly. In this talk, I will show how nonparametric smoothing techniques can be used to avoid this assumption. Model selection for PCCs will also be addressed within the proposed method.

Speaker

Elif F. Acar is a Postdoctoral Fellow in the Department of Mathematics and Statistics at McGill University. She holds a Ph.D. in Statistics from the University of Toronto.

Date: 2011-09-09

Time: 14:00-16:30

Location: UdeM, Pav. André-Aisenstadt, SALLE 1360

Abstract:

Susko: The data generated by large scale sequencing projects is complex, high-dimensional, multivariate discrete data. In studies of evolutionary biology, the parameter space of evolutionary trees is an unusual additional complication from a statistical perspective. In this talk I will briefly introduce the general approaches to utilizing sequence data in phylogenetic inference. A particular issue of interest in phylogenetic inference is assessments of uncertainty about the true tree or structures that might be present in it. The primary way in which uncertainty is assessed in practice is through bootstrap support (BP) for splits, large values indicating strong support for the split. A difficulty with this measure, however, has been deciding how large is large enough. We discuss the interpretation of BP and ways of adjusting it so that it has an interpretation similar to a p-value. A related issue, having to do with the behaviour of methods when data are generated from a star tree, gives rise to an interesting example in which, due to the unusual statistical nature,Bayesian and maximum likelihood methods give strikingly different results, even asymptotically.

Date: 2011-08-31

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Volume estimators based on Cavalieri’s principle are widely used in the bio- sciences. For example in neuroscience, where volumetric measurements of brain structures are of interest, systematic samples of serial sections are obtained by magnetic resonance imaging or by a physical cutting procedure. The volume v is then estimated by ˆv, which is the sum over the areas of the structure of interest in the section planes multiplied by the width of the sections, t > 0. Assessing the precision of such volume estimates is a question of great practical importance, but statistically a challenging task due to the strong spatial dependence of the data and typically small sample sizes. In this talk, an overview of classical and new approaches to this problem will be presented. A special focus will be given to some recent advances on distribution estimators and confidence intervals for ˆv; see Hall and Ziegel (2011).