Associate editors are appointed for 4-year periods and usually have a workload of no more than 10-12 new papers per year.

If you would like to nominate yourself, please send a short note to Mike Elliott (again, details at the link) with a CV by 31st October 2025.

We are looking to cover these areas:

Latent variables
Multivariate modes
Machine learning
Network analysis
Causal inference
Missing data
Confidentiality
Spatial statistics
Categorical data analysis
Infectious disease
Genomics
Econometrics
Sports statistics
Business management

At Hallam Conference Centre and online, more details on the link. *free and open to everyone*, and people can just listen to the authors and invited discussants, there's no need to make a comment.

High recidivism risk found for 20% of ever-jailed. 10% cycle back to jail up to twice per year. Little association to age, gender, crime type and race.

Most recidivism studies focus on prisons and occurrence in a discrete framework. Little is known about jail recurrence & time-to-recidivism. Barone and Farcomeni use novel latent class multi-state quantile regression with cure fraction methods on >550,000 US (2020–2023) jail records

They also illustrate the application of the proposed tree-based MI method using data from a cellphone survey on COVID-19 vaccination in Uganda, which represents a subcohort sample drawn from the 2020 Uganda Population-based HIV Impact Assessment Survey.

In this paper, authors propose a Bayesian tree-based multiple imputation (MI) approach for estimating population means using the Phase II sample, where the parent survey was conducted using a complex survey design, and with simulations they test the advantages of the approach

Empirical evaluations demonstrate that MTGCL outperforms existing graph contrastive learning models in classification accuracy across multiple time periods while maintaining competitive computational efficiency.

Data and Code are here : github.com/yuzhouguangc...

Fraud detection in blockchain networks presents unique challenges due to decentralized and pseudonymous nature of transactions. This study introduces a novel Multilayer Topology-Aware Graph Contrastive Learning (MTGCL) framework to detect fraudulent activity within the Ethereum transaction network

Their results suggest that overlooking latent homophily can lead to either underestimation or overestimation of causal peer influence, accompanied by considerable estimation uncertainty.

In this paper, authors address this challenge by leveraging latent locations inferred from the network to disentangle homophily from causal peer influence, and extend this approach to multiple networks by adopting a Bayesian hierarchical modelling framework

Researchers have focused on understanding how an individual’s behaviour is influenced by their peers behaviours. Identifying causal peer influence, is challenging due to confounding by homophily (people tend to connect with those who share similar characteristics)

Bonus: Model estimation can be carried out with the R package Bernadette, available on CRAN

(cran.r-project.org/web/packages...)

The temporal evolution of transmission rates in populations containing multiple types of individual is reconstructed via an appropriate dimension-reduction formulation driven by independent diffusion processes.

Authors propose practical guidelines, and present the performance of the proposed estimators in numerical studies in two sets of real data: exit polls from the 19th South Korean election and public data collected from the Korean Survey of Household Finances and Living Conditions

When survey non-response isn't random but depends on the unobserved answer itself, standard methods give biased results. Previous solutions required hard-to-find "instrumental variables" that researchers can't easily identify beforehand. 📊