Jamie Hill
Frequency-domain model selection for large-scale time series via Bayesian variable selection
This project aims to develop an efficient Bayesian algorithm for model selection in the frequency domain. The algorithm will jointly sample indicator variables in order to identify and remove irrelevant components while estimating their effects. Unlike conventional information-criterion-based approaches, which require separate estimation runs for each candidate model, our method simultaneously selects and averages across models within a single run, enabling faster and more comprehensive inference.
Jamie Hill
University of Technology Sydney
Jamie Hill is a third year student in Economics, Mathematics, and Language at the University of Technology, Sydney. He is extremely passionate about teaching and making complex topics more accessible and engaging. Jamie is hoping to follow his Bachelor’s degree in Economics with an Honours in Mathematics and to continue to postgraduate study to research in theoretical economics, econometrics, and pure mathematics. Outside of study, Jamie enjoys exploring new restaurants, skiing, rock climbing, and hiking through the Royal National Park.
