2017 Fall

Date: 2017-12-01 Time: 15:30-16:30 Location: BURN 1205 Abstract: Fisher’s method, also known as Fisher’s combined probability test, is commonly used in meta-analyses to combine p-values from the same test applied to K independent samples to evaluate a common null hypothesis. Here we propose to use it to combine p-values from different tests applied to the same sample in two settings: when jointly analyzing multiple genetic variants in set-based genetic association studies, or when jointly capturing main and interaction effects in the presence of missing one of the interacting variables.

Date: 2017-11-24 Time: 15:30-16:30 Location: LEA 232 Abstract: As Canada celebrates its 150th anniversary, it may be good to reflect on the past and future of data analysis and statistics in this country. In this talk, I will review the Victorian Statistics Movement and its effect in Canada, data analysis by a Montréal physician in the 1850s, a controversy over data analysis in the 1850s and 60s centred in Montréal, John A.

Date: 2017-11-17 Time: 15:30-16:30 Location: BURN 1205 Abstract: Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated. In this talk I will explain how a recently proposed functional pruning algorithm can be generalized to solve such constrained changepoint detection problems.

Date: 2017-11-10 Time: 15:30-16:30 Location: BURN 1205 Abstract: One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data. In light of this capacity for overfitting, it is remarkable that simple algorithms like SGD reliably return solutions with low test error. One roadblock to explaining these phenomena in terms of implicit regularization, structural properties of the solution, and/or easiness of the data is that many learning bounds are quantitatively vacuous when applied to networks learned by SGD in this “deep learning” regime.

Date: 2017-11-03 Time: 15:30-16:30 Location: BURN 1205 Abstract: In this talk, I will outline how to do (Bayesian) statistics. I will focus particularly on the things that need to be done before you see data, including prior specification and checking that your inference algorithm actually works. Speaker Daniel Simpson is an Assistant Professor in the Department of Statistical Sciences, University of Toronto

Date: 2017-10-27 Time: 15:30-16:30 Location: BURN 1205 Abstract: In many current applications, scientists can easily measure a very large number of variables (for example, hundreds of protein levels), some of which are expected be useful to explain or predict a specific response variable of interest. These potential explanatory variables are most likely to contain redundant or irrelevant information, and in many cases, their quality and reliability may be suspect. We developed two penalized robust regression estimators that can be used to identify a useful subset of explanatory variables to predict the response, while protecting the resulting estimator against possible aberrant observations in the data set.

Date: 2017-10-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Statistical optimization has generated quite some interest recently. It refers to the case where hidden and local convexity can be discovered in most cases for nonconvex problems, making polynomial algorithms possible. It relies on a careful analysis of the geometry near global optima. In this talk, I will explore this issue by focusing on sparse regression problems in high dimensions. A computational framework named iterative local adaptive majorize-minimization (I-LAMM) will be proposed to simultaneously control algorithmic complexity and statistical error.

Date: 2017-10-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: In various applications, evaluating spatial risks (such as floods, heatwaves or storms) is a key problem. The aim of this talk is to make use of extreme value theory and max-stable processes to provide quantitative answers to this issue. A review of the literature will be provided, as well as a wide comparative study based on a simulation design mimicking daily rainfall in France.

Date: 2017-09-29 Time: 14:30-16:30 Location: BURN 1205 Abstract: McNeil: In this talk we study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modelled probability level. The choice of the kernel function makes explicit the user’s priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage.

Date: 2017-09-22 Time: 14:00-15:00 Location: BRONF179 Abstract: We study the problem of nonparametric dependence detection. Many existing methods suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a filtration induced by marginal binary expansions. Through a novel decomposition of the likelihood of contingency tables whose sizes are powers of 2, we show that the interactions of binary variables in the filtration are complete sufficient statistics for dependence.