Spatial prediction of non-negative spatial processes using asymmetric losses

Distinguished Professor Noel Cressie visited RSFAS on Wednesday 24 April and because of the Anzac Day holiday, Noel gave his seminar a day earlier than usual. I was pleased to join around twenty others in person whose schedules accommodated the change in day.

Noel’s title is quite technical and much of his talk addressed formulations and theoretical results. Nonetheless as always his research has been motivated by pressing real problem, particularly in the area of climate change. One of the applications of the work discussed today was how to deal with predictions of extreme weather events e.g. flood levels, where underprediction has very different implications to overprediction. This is the asymmetric loss of the talk’s title.

The proposed asymmetric loss function in this talk was a power-divergence loss which i based on a ratio not a difference. The well-known Kullback-Leibler distance is a special case of this family of loss functions, giving it a nice grounding in familiar territory.

Noel illustrated the theory with an oldie-but-a-goodie example of zinc concentrations in soil on the floodplain of the River Meuse in the Netherlands. He also alluded to recent flood events in Australia to help bring local relevance to the examples.

Sample size for monitoring disease in free-ranging wildlife populations

Professor James Booth of Cornell University has been a longtime colleague of what is now the ANU Research School of Finance, Actuarial Studies & Statistics. it was a pleasure to attend his seminar in the School on Thursday 11 April. Around 25 people attended in person.

James’ talk reported on one of those conversations that turn into a full-blown methodology project. In this case, a conversation with a veterinary scientist led to sample size estimation for an evidence base on the eradication of chronic wasting disease in the free-ranging deer populations of the USA.

Neither a hypergeometric model nor a binomial model we’re going to account for the lack of independence between animals, so James ended up with a beta-binomial model. In answer to the question “What would Bayesians do?”, James then added priors on the beta distribution parameters. The final addition to the model was to take account of the fact that animals are not independent at all, but tend to live in family groups of four to five.

And the final answer? A sample maybe as large as 300, or maybe as small as 16, depending on correlations!

Enhancing Bayesian small area level methods with applications in health

It’s been a pleasure collaborating with the spatio-temporal researchers at QUT and the Cancer Atlas, in particular with Susanna Cramb and her PhD student Jamie Hogg. his PhD exit seminar was on Monday 8 April, and I joined over 25 people online (plus the crowd in person) to hear his presentation.

As always, Jamie’s talk did not disappoint. he took us through the pillars of his research, rooted in cancer and the burden of disease. His focus has also been on risk factors, modelled using small area methods right down to SA2 level. he has had to overcome several methodological hurdles around sparse data, the need to develop indices of risky behaviour, and the potential of a two stage logistic normal approach. MrP (multilevel regression and post-stratification) was also invited to the methodological party.

As well as all that, Jamie came second and first in two hackathons, made two trips to Canada to present his research, and attended another nine conferences (one of which was organised by my team, see the report here!) Congratulations on making to the final stages of a PhD Jamie, and all the best for the final write-up!

Optimal dynamic treatment regime inference – a tale of two methods

Dr Weichange Yu of the University of Melbourne came to RSFAS to present this seminar on Thursday 4 April. Around 25 people attended in person to hear this talk about management of patients with ongoing disease.

The two methods of the title were a maximum likelihood method, and a so-called Bayesian method although Weichang did say he thought the Bayesian method was not well named! nonetheless he took the audience through the detail of the expressions to be minimised, along with a couple of small examples to show how changing the treatment mid-way through a clinical trial could indeed be beneficial.