2025 is off to a good start. I will be working (again) with Freegle to continue where we were last time when collaborating under the RSS Statisticians for Society scheme. I have previously made a post about this. This time we’ve got funded by ICMS KE Catalyst!

The paper on conditional extremes model for graphs, led by my PhD student Aiden Farrell (jointly supervised with Emma Eastoe) is now on arXiv. While there have been multivariate extremes model for data observed on graphs, the novelty is the new approach for the residual distribution, which has usually been assumed multivariate gaussian. Here, we applied the multivariate asymmetric generalised Gaussian (MVAGG) distribution instead that enables fully parametric prediction. This complicates the computational inference, but Aiden developed a multi-step algorithm to address this issue.

Thank you to Teresa Lee at the Young Statisticians Section to invite me to present (slides) at the session “Getting your work to work” at the RSS conference. It’s a good opportunity for me to consolidate what I have been doing regarding reproducibility so far, in my teaching, research, and the current role of UKRN local network leads.

Fresh off the press, the paper on mixture distribution for network degrees is now available online on Statistica Neerlandica! The presentations I have recently made at Sunbelt Conference and an internal research day are based on the same paper, but we have changed the title to hopefully make it more appealing to the network science community.

First-timer at the INSNA Sunbelt Conference, this time in Edinburgh. It wasn’t ideal to present in the very last session on the last day of the conference, so thank you to those who turned up, and the numbers were indeed better than expected.

The inaugural UK Conference on Teaching Statistics (UKCOTS) took place last Thursday and Friday at Manchester. I didn’t know what to expect, but learned a whole lot and got inspired on what to do for my future module on data visualisation. I think I will try to implement (a subset of) the following things: