Rate My Professor Darfiana Nur

Dr. Nur's research specializations include nonlinear time series analysis and modelling, with a focus on Smooth Threshold Autoregressive (STAR) models and regime switching volatility models. Her academic interests also encompass Bayesian Markov Chain Monte Carlo (MCMC) algorithms, DNA sequence modelling in bioinformatics, Bayesian Hidden Markov Models (HMMs) in genetics, and deep neural networks for nonlinear time series. Expertise keywords highlight time series modelling, Bayesian inference, machine learning, and deep learning. She has produced 23 journal publications, many in high-ranked statistics journals, 10 refereed conference papers, and 35 non-refereed conference papers. Key publications are 'Coordinate gradient descent algorithm in adaptive LASSO for pure ARCH and pure GARCH models' (2025, Computational Statistics, with Nasir et al.), 'Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model' (2024, Statistical Papers, with Nasir et al.), 'Adaptive LASSO with coordinate gradient descent algorithm for M-BEKK-ARCH(q) model' (2024, AIP Conference Proceedings, with Nasir et al.), and 'Bayesian inference of multivariate-GARCH-BEKK models' (2023, Statistical Papers, with Livingston). Dr. Nur has supervised seven PhD students to completion (two at Newcastle, four at Flinders, one at UWA), nine Masters by Coursework, and seven Honours students, underscoring her influence in statistical research and education.