Statistical Learning of Noisy Data
Join WiM for a 45 Minute talk with Professor Grace Y. Yi and a Q&A session to follow.
Register today to attend the WiM Winter Colloquium 2022.
Abstract
Thanks to the advancement of modern technology in acquiring data, massive data with diverse features and big volumes 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, I will discuss some issues induced by noisy data and how they may complex statistical inferential procedures.
Speaker
Prof. Grace Y. Yi
Biography
Grace Y. Yi is a professor at the Department of Statistical and Actuarial Sciences and the Department of Computer Science at the University of Western Ontario. She currently holds a Tier I Canada Research Chair in Data Science. Dr. Yi'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 monograph “Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application” (2017, Springer), and co-edited “Handbook of Measurement Error Models” (with Aurore Delaigle and Paul Gustafson) (2021, Chapman & Hall/CRC).