Journal of the Royal Statistical Society: Series A

Call for Papers: Big Data before Data Science

📢 Call for Papers

The Journal of the Royal Statistical Society, Series A (@jrssa.bsky.social) invites submissions for a special issue exploring historical data practices and their impact on modern data science. @royalstatsoc.bsky.social

🔍 Explore the full call: oxford.ly/4rgfcdl

Bayesian evidence synthesis for modelling SARS-CoV-2 transmission Abstract. The acute phase of the COVID-19 pandemic has made apparent the need for decision support based upon accurate epidemic modelling. This process is

🆕Bayesian evidence synthesis for modelling SARS-CoV-2 transmission

🧪Apsemidis and Demiris adopt the Bayesian paradigm and synthesize publicly available data via a discrete-time stochastic epidemic modelling framework doi.org/10.1093/jrss...

We demonstrate that higher variability in snap timing is beneficial for the passing game, as it relates to facing less havoc created by the opposing defence. We also obtain a quarterback leaderboard based on our snap timing variability measure, and Patrick Mahomes stands out 🐐

Using a Bayesian multilevel model with heterogeneous variances, we provide an assessment of National Football League (NFL) quarterbacks and their ability to synchronize the timing of the ball snap with pre-snap movement from their teammates.

A multilevel model with heterogeneous variances for snap timing in the National Football League Abstract. Player tracking data have provided great opportunities to generate insights into understudied areas of American football, such as pre-snap motion

🆕A multilevel model with heterogeneous variances for snap timing in the National Football League

🏈Nguyen and Yurko snap into the passing lanes

doi.org/10.1093/jrss...

🤔New tools for network time series with an application to COVID-19 hospitalisations

Nason,Salnikov & Cortina-Borja present the following discussion paper (and you can find contributions to the discussion from other authors in the journal!) doi.org/10.1093/jrss...

Planning is underway for the 2026 International Conference which will take place in Bournemouth from 7-10 September.

Submissions are now welcomed for invited topic sessions and workshops with a deadline of 21 November. Full details: rss.org.uk/training-eve...

Join Series A tomorrow for what promises to be a lively discussion meeting on Carl Morris' Finite Selection Model and its applications throughout experimental design

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

rss.org.uk/news-publication/news-publications/2025/member-callouts/associate-editors-sought-for-series-a/

We are looking for new editors for JRSSA! (details at the link)

We are seeking associate editors for a four-year term starting in January 2026.

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.

We wanted to invite you to the next discussion paper meeting ‘Balanced and Robust Randomized Treatment Assignments: The Finite Selection Model for the Health Insurance Experiment and Beyond’ on October 22nd at 3 pm (UK time) (more details on link!)

rss.org.uk/training-eve...

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

Latent class multi-state quantile regression with a cure fraction: application to jail recidivism in the U.S. Abstract. We propose a multi-state quantile regression model that admits a cure-fraction for each possible transition, so that individuals may not experien

🆕Latent class multi-state quantile regression with a cure fraction: application to jail recidivism in the USA

💡@barross993.bsky.social and
@afarcome.bsky.social
analyze time spent after release with a quantile regression approach
doi.org/10.1093/jrss...

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

🆕Improving survey inference in two-phase designs using Bayesian machine learning

🪶Wang, Chen, et al with a very clear purpose in the title of their paper

doi.org/10.1093/jrss...

GitHub - yuzhouguangc/MTGCL Contribute to yuzhouguangc/MTGCL development by creating an account on GitHub.

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

Multilayer topology-aware graph contrastive learning for fraud detection in the Ethereum transaction network Abstract. Fraud detection in blockchain networks presents unique challenges due to the decentralized and pseudonymous nature of transactions. This study in

🆕Multilayer topology-aware graph contrastive learning for fraud detection in the Ethereum transaction network

💱Chen et al delve in Blockchain networks

doi.org/10.1093/jrss...

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)

🆕A Bayesian approach to estimate causal peer influence accounting for latent network homophily

💡Um , Sweet and Adhikari show a framework to disentangle homophily from causal peer influence

doi.org/10.1093/jrss...

Bernadette: Bayesian Inference and Model Selection for Stochastic Epidemics Bayesian analysis for stochastic extensions of non-linear dynamic systems using advanced computational algorithms. Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) ...

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.

🆕Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19
💡Bouranis et al. develop a flexible Bayesian evidence synthesis approach to model age-specific transmission dynamics of COVID-19 based on daily death counts

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