Statistics & Risk Modeling (STRM) aims to cover modern methods in statistics and probabilistic modeling, with a strong emphasis on machine learning, particularly statistical learning. The journal focuses on applications in finance, insurance, and related fields, addressing key aspects of modeling and risk management. STRM welcomes contributions that explore the uncertainty, risk, and regulation associated with artificial intelligence, as well as stochastic processes and other relevant statistical methods. We are particularly interested in papers that delve into the mathematical foundations of AI, its applications in statistical modeling, and its role in enhancing risk management strategies.
⚡ Speed vs Prestige
How does this journal balance review speed with impact level?
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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2024
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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.
Modeling and SimulationQ3
Statistics and ProbabilityQ3
Statistics, Probability and UncertaintyQ3
Subject Classification
Web of Science Categories
Statistics & Probability
Scopus Categories
Statistics, Probability and UncertaintyStatistics and ProbabilityModeling and Simulation
Research Topics (OpenAlex)
Human auditory perception and evaluationAdvanced Statistical Methods and ModelsStochastic processes and financial applicationsEducational Robotics and EngineeringEarthquake and Disaster Impact StudiesStatistical Methods and InferenceRisk and Portfolio OptimizationProbability and Risk ModelsDiverse Scientific and Economic StudiesFinancial Risk and Volatility Modeling