# Events - September 2018

Saturday, September 29, 2018 — 8:00 AM to 6:00 PM EDT

## Waterloo Datathon - Fall 2018

We are extending an invitation to a select group of talented undergraduate, graduate and PhD students to participate in the upcoming University of Waterloo Datathon.

Thursday, September 27, 2018 — 4:00 PM EDT

## Department seminar by Yuanying Guan, Indiana University Northwest

Agent-based Asset Pricing, Learning, and Chaos

The Lucas asset pricing model is one of the most studied model in financial economics in the past decade. In our research, we relax the original assumptions in Lucas model of homogeneous agents and rational expectations. We populate an artificial economy with heterogeneous and boundedly rational agents. By defining a Correct Expectations Equilibrium, agents are able to compute their policy functions and the equilibrium pricing function without perfect information about the market. A natural adaptive learning scheme is given to agents to update their predictions. We examine the convergence of equilibrium with this learning scheme and show that the equilibrium is learnable (convergent) under certain parameter combinations. We also investigate the market dynamics when agents are out of equilibrium, including the cases where prices have excess volatility and the trading volume is high. Numerical simulations show that our system exhibits rich dynamics, including a whole cascade from period doubling bifurcations to chaos.

Thursday, September 20, 2018 — 4:00 PM EDT

## Department seminar by Michele Guindani, University of California

Bayesian Approaches to Dynamic Model Selection

In many applications, investigators monitor processes that  vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or normal" behavior. In this talk, I will first discuss a principled Bayesian approach for estimating time varying functional connectivity networks from brain fMRI data. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. Then, I will propose a Bayesian nonparametric model selection approach with an application to the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States.  More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior  which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences  are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations.  We show how the proposed modeling framework performs  in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for  detecting outbreaks over time, with more recent Markov switching based models, and with other Bayesian nonparametric priors that do not take into account spatio-temporal dependence.

Thursday, September 13, 2018 — 4:00 PM EDT

## Department seminar by Karen Kafadar, University of Virginia

The Critical Role of Statistics in Evaluating Forensic Evidence

Statisticians have been important contributors to many areas of science, including chemistry (chemometrics), biology (genomics),  medicine (clinical trials), and agriculture (crop yield), leading  to valuable advances in statistical research that have benefitted multiple fields (e.g., spectral analysis, penalized regression,  sequential analysis, experimental design).  Yet the involvement of statistics specifically in forensic science has not been nearly as extensive, especially in view of its importance (ensuring proper administration of justice) and the value it has demonstrated thus far (e.g., forensic DNA, assessment of bullet lead evidence, significance of findings in the U.S. anthrax investigation,  reliability of eyewitness identification).  Forensic methods in many areas remain unvalidated, as recent investigations have highlighted (notably, bite marks and hair analysis).  In this talk, I will provide three examples (including one with data kindly provided by Canadian Forensic Service) where statistics plays a vital role in evaluating forensic evidence and motivate statistical research that can have both theoretical and practical value, and ultimately strengthen forensic evidence.

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