2016 Fall

Date: 2016-10-07 Time: 15:30-16:30 Location: BURN 1205 Abstract: Suppose that binary classification is done by a tree method in which the leaves of a tree correspond to a partition of d-space. Within a partition, a majority vote is used. Suppose furthermore that this tree must be constructed recursively by implementing just two functions, so that the construction can be carried out in parallel by using “cells”: first of all, given input data, a cell must decide whether it will become a leaf or internal node in the tree.

Date: 2016-09-30 Time: 15:30-16:30 Location: BURN 1205 Abstract: Much theoretical and applied work has been devoted to high-dimensional regression with clean data. However, we often face corrupted data in many applications where missing data and measurement errors cannot be ignored. Loh and Wainwright (2012) proposed a non-convex modification of the Lasso for doing high-dimensional regression with noisy and missing data. It is generally agreed that the virtues of convexity contribute fundamentally the success and popularity of the Lasso.

Date: 2016-09-23 Time: 15:30-16:30 Location: BURN 1205 Abstract: We revisit the problem of estimating the intensity parameter of a homogeneous Poisson point process observed in a bounded window of $R^d$ making use of a (now) old idea going back to James and Stein. For this, we prove an integration by parts formula for functionals defined on the Poisson space. This formula extends the one obtained by Privault and Réveillac (Statistical inference for Stochastic Processes, 2009) in the one-dimensional case and is well-suited to a notion of derivative of Poisson functionals which satisfy the chain rule.

Date: 2016-09-16 Time: 16:00-17:00 Location: LB-921.04, Library Building, Concordia Univ. Abstract: There are some time series which exhibit long-range dependence as noticed by Hurst in his investigations of river water levels along Nile river. Long-range dependence is connected with the concept of self-similarity in that increments of a self-similar process with stationary increments exhibit long-range dependence under some conditions. Fractional Brownian motion is an example of such a process. We discuss statistical inference for stochastic processes modeled by stochastic differential equations driven by a fractional Brownian motion.

Date: 2016-09-09 Time: 15:30-16:30 Location: BURN 1205 Abstract: Goria and Flury (Definition 2.1, 1996) proposed the two-set canonical variate model (referred to as the CV-2 model hereafter) and its extension in multiple populations with invariant weight coefficients (Definition 2.2). The equality constraints imposed on the weight coefficients are in line with the approach to interpreting the canonical variates (i.e., the linear combinations of original variables) advocated by Harris (1975, 1989), Rencher (1988, 1992), and Rencher and Christensen (2003).