2015 Winter

Date: 2015-01-16 Time: 15:30-16:30 Location: BURN 1205 Abstract: We consider the white noise representation of functional data taken as i.i.d. realizations of a Gaussian process. The main idea is to establish an asymptotic equivalence in Le Cam’s sense between an experiment which simultaneously describes these realizations and a collection of white noise models. In this context, we project onto an arbitrary basis and apply a novel variant of Stein-type estimation for optimal recovery of the realized trajectories.

Date: 2015-01-15 Time: 16:00-17:00 Location: CRM 1360 (U. de Montréal) Abstract: Functional data analysis (FDA) has received substantial attention, with applications arising from various disciplines, such as engineering, public health, finance etc. In general, the FDA approaches focus on nonparametric underlying models that assume the data are observed from realizations of stochastic processes satisfying some regularity conditions, e.g., smoothness constraints. The estimation and inference procedures usually do not depend on merely a finite number of parameters, which contrasts with parametric models, and exploit techniques, such as smoothing methods and dimension reduction, that allow data to speak for themselves.

Date: 2015-01-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: A mixture of coalesced generalized hyperbolic distributions is developed by joining a finite mixture of generalized hyperbolic distributions with a mixture of multiple scaled generalized hyperbolic distributions. The result is a mixture of mixtures with shared model parameters and common mode. We begin by discussing the generalized hyperbolic distribution, which has the t, Gaussian and others as special cases. The generalized hyperbolic distribution can represented as a normal-variance mixture using a generalized inverse Gaussian distribution.

Date: 2015-01-09 Time: 15:30-16:30 Location: BURN 1205 Abstract: Given the discouraging state of current efforts to curb global warming, we can imagine that we will soon turn our attention to mitigation. On a global scale, distressed populations will turn to national and international organizations for solutions to dramatic problems caused by climate change. These institutions in turn will mandate the collection of data on a scale and resolution that will present extraordinary statistical and computational challenges to those of us viewed as having the appropriate expertise.