Analysis of High-Dimensional Data (MAST90110)

Modern data sets are growing in size and complexity due to the astonishing development of data acquisition and storage capabilities. This subject focuses on developing rigorous statistical learning methods that are needed to extract relevant features from large data sets, assess the reliability of the selected features, and obtain accurate inferences and predictions. This subject covers recent methodological developments in this area such as inference for high-dimensional inference regression, empirical Bayes methods, model selection and model combining methods, and post-selection inference methods.