# Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is among the top academic units for statistical and actuarial science in the world and is home to more than 40 research active full-time faculty working in diverse and exciting areas. The Department is also home to over 900 undergraduate students and about 150 graduate students in programs including Actuarial Science, Biostatistics, Quantitative Finance, Statistics, and Statistics-Computing.

We are located on University of Waterloo main campus, which is located at the heart of Canada's Technology Triangle about 100 kilometers west of Toronto.

1. July 19, 2018$3.7M in funding for AI and medical imaging project Source: Daily Bulletin. The University of Waterloo's Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) announced in May 2018 that its AI project for digital pathology has been awarded a grant by the Ontario Research Fund – Research Excellence program (ORF-RE). The project aims to develop an intelligent search engine for digital pathology that can retrieve relevant cases from large archives, auto-caption the images, and facilitate consensus building. The Ontario government will fund the 5-year project with a grant in amount of$3.2M. Huron Digital Pathology, as the industrial partner of Kimia Lab, will contribute $500k to the project. The company is the only Canadian manufacturer of digital scanners for pathology. Four professors from the University of Waterloo (Mark Crowley, Ali Ghodsi, Oleg Mikhailovich, and Hamid Tizhoosh), together with the machine learning group at the University of Guelph led by professor Graham Taylor (Vector Institute), and professor Shahryar Rahnamayan (UOIT) will collaborate with three hospitals to design and test an advanced search engine for large pathology archives. Grand River Hospital (Kitchener, ON), Southlake Regional Health Centre (Newmarket, ON) and University of Pittsburgh Medical Center (PA, USA) will not only provide data but also validate the results of the project. 2. July 16, 2018Nursing notes can help indicate whether ICU patients will survive Researchers at the University of Waterloo have found that sentiments in the nursing notes of health care providers are good indicators of whether intensive care unit (ICU) patients will survive. Hospitals typically use severity of illness scores to predict the 30-day survival of ICU patients. These scores include lab results, vital signs, and physiological and demographic characteristics gathered within 24 hours of admission. 3. July 16, 2018Congratulations Professor Ruodu Wang, recipient of the NSERC DAC award! Professor Ruodu Wang received a$120,000 Discovery Accelerator Supplement (DAC) from the Natural Sciences and Engineering Research Council (NSERC) for a proposal titled “Model Uncertainty and Robustness in Risk Management.”

1. Aug. 2, 2018Department seminar by Daniel Farewell, Cardiff University

No Such Thing as Missing Data

The phrase "missing data" has come to mean "information we really, really wish we had". But is it actually data, and is it actually missing? I will discuss the practical implications of taking a different philosophical perspective, and demonstrate the use of a simple model for informative observation in longitudinal studies that does not require any notion of missing data.

2. Aug. 8, 2018Department seminar by Yang Li, Renmin University

Model Confidence Bounds for Variable Selection

In this article, we introduce the concept of model confidence bounds (MCBs) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCBs identify two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCBs proposes a group of nested models as candidates and the MCBs’ width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool — the model uncertainty curve (MUC) — is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCBs methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and a real data example confirm the validity and illustrate the advantages of the proposed method.

3. Sep. 27, 2018Department 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.

All upcoming events

## Riley Metzger

Lecturer

Contact Information:
Riley Metzger

Research interests

Statistics education.