CRM-Colloquium

• • Oct 14, 2011
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  Dupuis: Modeling non-stationary extremes: The case of heat waves | Davis: Estimating extremal dependence in time series via the extremogram

  Debbie Dupuis and Richard A. Davis · Oct 14, 2011

  Date: 2011-10-14

  Time: 14:00-16:30

  Location: TROTTIER 1080

  Abstract:

  Dupuis: Environmental processes are often non-stationary since climate patterns cause systematic seasonal effects and long-term climate changes cause trends. The usual limit models are not applicable for non-stationary processes, but models from standard extreme value theory can be used along with statistical modeling to provide useful inference. Traditional approaches include letting model parameters be a function of covariates or using time-varying thresholds. These approaches are inadequate for the study of heat waves however and we show how a recent pre-processing approach by Eastoe and Tawn (2009) can be used in conjunction with an innovative change-point analysis to model daily maximum temperature. The model is then fitted to data from four U.S. cities and used to estimate the recurrence probabilities of runs over seasonally high temperatures. We show that the probability of long and intense heat waves has increased considerably over 50 years.

• • Sep 9, 2011
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  Susko: Properties of Bayesian posteriors and bootstrap support in phylogenetic inference | Labbe: An integrated hierarchical Bayesian model for multivariate eQTL genetic mapping

  Ed Susko and Aurélie Labbe · Sep 9, 2011

  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.