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Welcome! I am a PhD candidate at McGill University, Department of Mathematics and Statistics, supervised by Dr. David Alan Stephens.
Currently I am working on projects involved in Bayesian computational methods applied in spatial point process. I am also interested in applied problems in medical science, business, and sociology.
I am also a statistical consultant at the Data Science Solutions Hub (DaSSH), which is operated under the McGill Computational and Data Systems Initiative.
Phone: (+1) 438-373-4043
Office: Burnside Hall, Room 1032, 805 Rue Sherbrooke Ouest, Montreal, H3A 09B
Education: BSc (2020) McGill University, Statistics and Computer Science
MSc (2022) McGill University, Mathematics and Statistics
PhD (Expected 2027) McGill University, Mathematics and Statistics
Email: peiyuan DOT huang AT mail DOT mcgill DOT ca
A simulation of log Gaussian Cox process (LGCP) data with exponential correlation. The associated simulated random intensity functions are shown in grey scale. See Moller (2005) for more details.
Comparison of multiple imputation and Bayesian method in mark-recapture-recovery model when covariates are missing not at random. This is a simulation study with 10 years of annual follow up. The figure shows the plot of the fitted survival function at different age groups, using both multiple imputation (M1) and Bayesian methods (M1B). Note that the black curves are fitted survival functions, and the red curve is the survival function under simulation. See Worthington et al (2014) for more details.
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