The Canadian Journal of Statistics Award 2020
The Canadian Journal of Statistics Award is presented each year by the Statistical Society of Canada to the author(s) of an article published in the journal, in recognition of the outstanding quality of the methodological innovation and presentation. This year’s winner is the article entitled “Doubly sparse regression incorporating graphical structure among predictors”. (Volume 47, no. 4, pp. 729–747) by M. Stephenson, R. A. Ali and G. A. Darlington.
When biological data is associated with a complex system, then it is advantageous to exploit the structure of the system to predict the biological response. The paper presents a novel approach, doubly sparse regression incorporating graphical structure (DSRIG), that first models the underlying structure of the variables in the system, and then leverages this structural information to improve prediction of the response. This model is highly flexible and has excellent predictive abilities compared to other methods, particularly when only a fraction of the variables are related to the response. It can help identify relevant predictors as well as potential predictors that may warrant further scrutiny for understanding the response. DSRIG is valid in both high-dimensional settings, where the number of variables exceeds the sample size, and in settings in which some predictors are highly correlated with one another.
Matthew Stephenson received his MSc and PhD degrees in Statistics from the University of Guelph in 2014 and 2019, respectively. His areas of research interest include statistical machine learning, regularized regression, biostatistics and bioinformatics.
R. Ayesha Ali completed her PhD in Statistics at the University of Washington in 2002 and is now an Associate Professor in the Department of Mathematics and Statistics at the University of Guelph. Her research involves statistical methods for complex high dimensional systems across diverse domains. Specific applications include biostatistics, animal health, and ecology.
Gerarda A. Darlington completed her PhD in Statistics at the University of Waterloo and is now a Full Professor in the Department of Mathematics and Statistics at the University of Guelph. Her research interests include statistical methods for correlated observations, methods for epidemiologic studies and the design and analysis of cluster randomized trials. She has been honoured as one of Guelph's Women of Distinction for her role as a mentor to women in STEM fields; she is also the recipient of the University of Guelph’s 2018 John Bell Award in recognition of her outstanding contributions to university education.
The citation for the award reads:
The article entitled “Doubly sparse regression incorporating graphical structure among predictors” by Matthew Stephenson, R. Ayesha Ali and Gerarda A. Darlington is recognized for an excellent presentation of impressive methodological development and application in machine learning.
Angelo Canty was primarily responsible for producing this material.