Seminar: 1:00-2:30pm EST
Student Session: 3:00-4:00 EST
Learning Noisy Data
Thanks to the advancement of modern technology in acquiring data, massive data with diverse features and big volume are becoming more accessible than ever. The impact of big data is significant. While the abundant volume of data presents great opportunities for researchers to extract useful information for new knowledge gain and sensible decision making, big data present great challenges. A very important, yet sometimes overlooked issue is the quality and provenance of the data. Big data are not automatically useful; big data are often raw and involve considerable noise.
Typically, the challenges presented by noisy data with measurement error, missing observations and high dimensionality are particularly intriguing. Noisy data with these features arise ubiquitously from various fields including health sciences, epidemiological studies, environmental studies, survey research, economics, and so on. In this talk, Grace will discuss some issues induced from noisy data and how they may complicate inferential procedures.
Grace Y. Yi is a professor of the Department Statistical and Actuarial Sciences and the Department of Computer Science at the University of Western Ontario where she currently holds a Tier I Canada Research Chair in Data Science. Grace’s research interests focus on developing statistical methodology to address challenges concerning measurement error, causal inference, imaging data, missing data, high dimensional data, survival data, and longitudinal data. She authored the manuscript “Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application” (2017, Springer).
Grace received her Ph.D. in Statistics from the University of Toronto in 2000 and then joined the University of Waterloo as a postdoctoral fellow (2000-2001), Assistant Professor (2001-2004), Associate Professor (2004-2010), Professor (2010-2019), and University Research Chair (2011-2018). She is a Fellow of the Institute of Mathematical Statistics, Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. In 2010 Grace received the Centre de Recherches Mathmatiques and the Statistical Society of Canada (CRM-SSC) Prize which recognizes a statistical scientist’s excellence and accomplishments in research during the first fifteen years after earning their doctorate. She was a recipient of the University Faculty Award (2004-2009) granted by the Natural Sciences and Engineering Research Council of Canada.
Grace’s work with Xianming Tan and Runze Li won The Canadian Journal of Statistics Award in 2016. Grace has served the professions in various capacities. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018). She now acts as the Editor of the Statistical Methodology section for the Journal of New England of Statistics in Data Science. Grace was President of the Biostatistics Section of the Statistical Society of Canada in 2016, and the Founder of the first chapter (Canada Chapter, established in 2012) of International Chinese Statistical Association. She is currently President Elect of the Statistical Society of Canada.
The student session after the talk will allow students to ask Grace questions about her research, the talk, the recommended paper or career opportunities. If you’re a student, make sure to register for this session.
There are two papers and a book chapter this month:
Chapter 1 from Handbook of Measurement Error Models, edited by Grace Y. Yi, Aurore Delaigle, Paul Gustafson.
Brakenhoff TB, Mitroiu M, Keogh RH, Moons KGM, Groenwold RHH, van Smeden M. Measurement error is often neglected in medical literature: a systematic review. J Clin Epidemiol. 2018 Jun;98:89-97. doi: 10.1016/j.jclinepi.2018.02.023. Epub 2018 Mar 6. PMID: 29522827. https://www.jclinepi.com/article/S0895-4356(17)31083-1/fulltext
Maarten van Smeden, Timothy L Lash, Rolf H H Groenwold, Reflection on modern methods: five myths about measurement error in epidemiological research, International Journal of Epidemiology, Volume 49, Issue 1, February 2020, Pages 338-347, https://doi.org/10.1093/ije/dyz251