Online Course on Model Averaging

There is now an online course to go with my book on model averaging. This course provides an up-to date overview of the topic, from both the Bayesian and frequentist perspective, including methods that are popular in machine learning, such as bagging and stacking. Recent developments, published since the book came out in 2018, are also covered, including confidenceContinue reading “Online Course on Model Averaging”

Faster bootstrap intervals for indices of relative abundance

I recently provided advice on the use of “single-fit” bootstrapping to obtain confidence intervals for indices of relative abundance, when fitting a delta-lognormal model to fisheries data. The key idea is that of resampling the model parameters from a multivariate normal distribution. This is computationally nice as it allows a parametric bootstrap confidence interval toContinue reading “Faster bootstrap intervals for indices of relative abundance”

Estimating percentiles in air dispersion modelling

I have recently been providing advice to STIMBR (Stakeholders in Methyl Bromide Reduction) on the estimation of percentiles when modelling the dispersion of chemicals in the atmosphere. It was interesting to see just how unreliably the highest upper percentiles of a skewed distribution are estimated, even from a very large sample. This has implications forContinue reading “Estimating percentiles in air dispersion modelling”

Testing lack-of-fit in phylogenetics

I am currently working with David Bryant at Otago University on testing lack-of-fit of a model in phylogenetics. It’s great to be able to use my research experience in estimating overdispersion on an important scientific problem. One of the key aspects of the problem is the sparseness in the data, which are multinomial with aContinue reading “Testing lack-of-fit in phylogenetics”