This year’s winner is the article entitled “Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error.” (Volume 43, no. 4, pp. 498-518) by Grace Y. Yi, Xianming Tan and Runze Li.

In longitudinal data analysis it is common to want to do model selection. This is a particularly challenging problem in the face of missing data and measurement error. This paper proposes marginal methods to do model selection and estimation at the same time while allowing for missing responses and error-prone covariates. It provides a unified framework using marginal generalized linear models. The work avoids full distributional assumptions for the response process and for the distribution of the true covariates. Committee members praised the combination of applicability, strong theoretical work and solid practical assessment of the proposed methods.

Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. Her broad research interests include measurement error models, missing data problems, high dimensional data analysis, survival data and longitudinal data analysis, estimating function and likelihood methods, and medical applications. Grace received her PhD in Statistics from the University of Toronto in 2000. She is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. She is the editor of The Canadian Journal of Statistics (2016-2018). She is President-Elect of the Biostatistics Section of Statistical Society of Canada in 2015, and the Founder and President of the first chapter (Canada Chapter) of the International Chinese Statistical Association.

Xianming Tan is a Research Associate Professor of Biostatistics at the University of North Carolina at Chapel Hill. He received his PhD in Statistics in 2005 from the Nankai University in China, and had postdoctoral training at Queen’s University and then at Penn State University, State College. Before moving to Chapel Hill, he was a biostatistician at the McGill University Health Centre, Montreal. His research interests focus on design and analysis of clinical trials, finite mixture models, variable selection for longitudinal studies and their applications to interdisciplinary research.

Runze Li is the Verne M. Willaman Professor of Statistics at Pennsylvania State University. He received his PhD in statistics from University of North Carolina at Chapel Hill in 2000. He is a fellow of IMS and ASA. He was the co-editor of the Annals of Statistics from 2013-2015. His current research concentrates on developing effective statistical procedures for highdimensional data analysis, including variable selection, feature screening and hypothesis testing. He is also interested in applying these statistical procedures for analyzing real-life high-dimensional data such as genetic data analysis and functional MRI data analysis. His other research interests include non- and semiparametric modeling and statistical applications to scientific research in social behavioural science and engineering.