WiM Winter Colloquium 2022: 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 to attend 

Speaker

Grace Y. Yi, University of Western Ontario

Title

Statistical Learning of Noisy Data

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 presents 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 cause complex statistical inferential procedures.