Bachelor of Science and Bachelor of Arts degree in Statistics (Hon.) and Mathematics from the University of Queensland in 2015.
I’m a statistician with broad interests in Bayesian statistical and machine learning techniques (see my website for more information).
Articles and Pre-prints
- Liquet, B., K. Mengersen, A. N. Pettitt, and M. Sutton. 2017. “Bayesian Variable Selection Regression of Multivariate Responses for Group Data.” Bayesian Analysis 12 (4). International Society for Bayesian Analysis: 1039–67. (https://projecteuclid.org/euclid.ba/1508983455)
- Micheaux, Pierre Lafaye de, Benoit Liquet, and Matthew Sutton. 2018. “A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data.” arXiv [stat.ML]. arXiv. http://arxiv.org/abs/1702.07066.
- Matthew Sutton, Rodolphe Thiébaut, Benoit Liquet. 2018. “Sparse partial least squares with group and subgroup structure” (Submitted Statistics in Medicine)
- Matthew Sutton, Kerrie Mengersen, Benoit Liquet. 2018. “Sparse Subspace Constrained Partial Least Squares” (Submitted Journal of Statistical Computation and Simulation)
Variable Selection for Structured Large Datasets
Over the last century, new technologies have brought about the study of large datasets in multiple disciplines, both research-based and in industry. In this research we consider large datasets that are common in biomedical research, typically these datasets contain observations of: genes (genomics), mRNA (transcriptomics), proteins (proteomics) and meabolites (metabolomics) and collectively are known as ‘Omics’ datasets. Two parallel avenues of the research are: the development of feature selection methods for large structured data; and the application of these methods for Omics datasets. These methods will be based on variable selection techniques developed in both frequentist and Bayesian paradigms.