The Journal of Computational and Graphical Statistics publishes research on statistical methods and applications that involve computation and graphical representation. Recent articles focus on developing computationally efficient algorithms and theoretical frameworks for statistical modeling, including Bayesian inference, machine learning, and data analysis. Topics include scalable methods for large datasets, improved estimation techniques, and the application of these methods to diverse data types and scientific problems.
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Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
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.
Discrete Mathematics and CombinatoricsQ1
Statistics and ProbabilityQ1
Statistics, Probability and UncertaintyQ1
Subject Classification
Web of Science Categories
Statistics & Probability
Scopus Categories
Statistics, Probability and UncertaintyDiscrete Mathematics and CombinatoricsStatistics and Probability
Research Topics (OpenAlex)
Statistical Methods and InferenceBayesian Methods and Mixture ModelsStatistical Methods and Bayesian InferenceAdvanced Statistical Methods and ModelsMarkov Chains and Monte Carlo Methods