2011 Fall

Date: 2011-10-07

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In this talk, the following topics will be discussed: A class of bivariate probability integral transforms and Kendall distribution; bivariate quantile curves, central and lateral regions; non-exchangeability and radial asymmetry identification; new measures of nonexchangeability and radial asymmetry; financial applications and a few open problems (joint work with Flavio Ferreira).

Speaker

Nikolai Kolev is a Professor of Statistics at the University of Sao Paulo, Brazil.

Date: 2011-09-30

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Streaming data is ubiquitous in a wide range of areas from engineering and information technology, finance, and commerce, to atmospheric physics, and earth sciences. The online approximation of properties of data streams is of great interest, but this approximation process is hindered by the sheer size of the data and the speed at which it is generated. Data stream algorithms typically allow only one pass over the data, and maintain sub-linear representations of the data from which target properties can be inferred with high efficiency.

Date: 2011-09-23

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In this talk, we give a basic introduction to Sumio Watanabe’s Singular Learning Theory, as outlined in his book “Algebraic Geometry and Statistical Learning Theory”. Watanabe’s key insight to studying singular models was to use a deep result in algebraic geometry known as Hironaka’s Resolution of Singularities. This result allows him to reparametrize the model in a normal form so that central limit theorems can be applied. In the second half of the talk, we discuss new algebraic methods where we define fiber ideals for discrete/Gaussian models. We show that the key to understanding the singular model lies in monomializing its fiber ideal.

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).