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文献信息
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. While having a high quality reviewing process we promise fast reports, whenever possible.
vol.42 (2025)
vol.41 (2024)
vol.40 (2023)
vol.39 (2022)
vol.38 (2021)
vol.37 (2020)
vol.36 (2019)
vol.35 (2018)
vol.34 (2017)
vol.33 (2016)
vol.32 (2015)
vol.31 (2014)
vol.30 (2013)
vol.29 (2012)
vol.28 (2011)
vol.27 (2009)
vol.26 (2008)
vol.25 (2007)
vol.24 (2006)
vol.23 (2005)