报告人简介：

Grace Y. Yi is a Professor and Tier I Canada Research Chair in Data Science at the University of Western Ontario. Her research interests focus on statistical methodology to address challenges such as measurement error, causal inference, imaging data, missing data, high-dimensional data, survival data, longitudinal data, and statistical machine learning. 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" (Grace Y. Yi, Aurore Delaigle, and Paul Gustafson, 2021, Chapman & Hall/CRC).

Dr. Yi received her Ph.D. in Statistics from the University of Toronto in 2000 and then joined the University of Waterloo, assuming various positions, including University Research Chair, until 2019. She is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. In 2010, Dr. Yi received the Centre de Recherches Mathématiques 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 also a recipient of the University Faculty Award (2004-2009) granted by the Natural Sciences and Engineering Research Council of Canada.

Dr. Yi has served the professions in various capacities. She is currently a Co-Editor-in-Chief of The Electronic Journal of Statistics (2022-2024) and the Editor of the Statistical Methodology and Theory Section for The New England Journal of Statistics in Data Science. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018). Dr. Yi was President of the Statistical Society of Canada (2021-2022) and is currently the Chair of the Lifetime Data Science Section of the American Statistical Association. She was also President of the Biostatistics Section of the Statistical Society of Canada (2016-2017) and the Founder of the first chapter (Canada Chapter, established in 2012) of the International Chinese Statistical Association.

摘要：

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, etc. In this talk, I will discuss some issues induced by noisy data and how they may complicate statistical inferential procedures.