The Journal of the Royal Statistical Society, Series B: Statistical Methodology, focuses on the development and application of statistical methodology. Recent articles highlight advancements in areas such as graphical models for high-dimensional data, methods for causal inference and treatment effect heterogeneity, and techniques for analyzing dynamic networks and time series data. The journal also features research on experimental design, model selection, and machine learning inference, including deep learning approaches and robustness in distributed data settings.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
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2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Statistics and ProbabilityQ1
Statistics, Probability and UncertaintyQ1
Subject Classification
Web of Science Categories
Statistics & Probability
Scopus Categories
Statistics, Probability and UncertaintyStatistics and Probability
Research Topics (OpenAlex)
Statistical Methods and InferenceAdvanced Statistical Methods and ModelsStatistical Methods and Bayesian InferenceBayesian Methods and Mixture ModelsOptimal Experimental Design MethodsStatistical Distribution Estimation and ApplicationsBayesian Modeling and Causal InferenceStatistical Methods in Clinical TrialsAdvanced Statistical Process MonitoringAdvanced Causal Inference Techniques