Rate My Professor Yang Yang

Dr. Yang's research specializations encompass functional data analysis, time series analysis, demographic forecasting, climate data modelling, and functional data tools for health data. He has contributed significantly to statistical methodology through peer-reviewed publications in high-impact journals. Key works include 'Does Climate Sensitivity Differ Across Regions? A Varying–Coefficient Approach' in the Journal of Business and Economic Statistics (2026, co-authored with Anderson et al.); 'Is the age pension in Australia sustainable and fair? Evidence from forecasting the old-age dependency ratio using the Hamilton-Perry model' in the Journal of Population Research (2025, with Chen and Shang); 'Forecasting high-dimensional functional time series with dual-factor structures' in the Journal of the Royal Statistical Society Series A: Statistics in Society (2025, with Tang et al.); 'Nonstationary Functional Time Series Forecasting' in the Journal of Forecasting (2025, with Shang); 'Mortality models ensemble via Shapley value' in Decisions in Economics and Finance (2024, with Bimonte et al.); 'Forecasting Australian fertility by age, region, and birthplace' in the International Journal of Forecasting (2024, with Shang and Raymer); 'Temporal and spatial Taylor's law: Application to Japanese subnational mortality rates' in the Journal of the Royal Statistical Society Series A: Statistics in Society (2022, with Shang and Cohen); and 'Feature extraction for functional time series: Theory and application to NIR spectroscopy data' in the Journal of Multivariate Analysis (2022, with Shang). In 2015, he was awarded the ANU RSFAS Honours Scholarship. His collaborative research spans institutions in Australia, the United Kingdom, Italy, and the United States.