Rate My Professor Alan Huang

Huang's research centers on statistical modeling, particularly generalized linear models, nonparametric and semiparametric models, count regression, time-series modeling, and empirical likelihood methods. He has contributed to interdisciplinary applications, including funded projects such as Analytics for the Australian Grains Industry (2023-2027) and Organ Transplantation as a Model of Reversible Frailty (2023-2026), which examines clinical changes in frailty through kidney transplantation. Notable publications include 'Joint estimation of the mean and error distribution in generalized linear models' (Journal of the American Statistical Association, 2014), 'Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts' (Statistical Modelling, 2017), 'Orthogonality of the mean and error distribution in generalized linear models' (Communications in Statistics - Theory and Methods, 2017), 'On arbitrarily underdispersed discrete distributions' (The American Statistician, 2022), 'Consistent second-order discrete kernel smoothing using dispersed Conway–Maxwell–Poisson kernels' (Computational Statistics, 2021), 'A fast look-up method for Bayesian mean-parameterised Conway-Maxwell-Poisson regression models' (Statistics and Computing, 2023), and 'On Exponential-Family INGARCH Models' (Journal of Time Series Analysis, 2025). Additional works cover robust permutation tests, density estimation, and collaborations on bacterial biofilms and Great Barrier Reef water quality monitoring. Huang's contributions enhance handling of overdispersed and underdispersed count data, influencing advancements in Bayesian regression and kernel smoothing techniques.