Journal of the Royal Statistical Society: Series B (Statistical Methodology)Journal Home
The Journal of the Royal Statistical Society, Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. Papers should contribute to the understanding of statistical methodology and/or develop and improve statistical methods; any mathematical theory should be directed towards these aims. Many papers published in Series B present new methods of collecting or analysing data, with associated theory, an indication of the scope of application, and a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing, statistical computation or simulation where original methodology is involved, and original contributions to the foundations of statistical science. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains, or if it is dominated by computations or simulations of a routine nature.
Publishing Model:Hybrid OA
ISSN:1369-7412
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EISSN:1467-9868
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Publication Frequency:Seven times a year
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Last updated on  01 Feb 2026 / Current volume: 躭躭 / Current issue:
More Details
Journal Disciplines
Probability Theory and Mathematical Statistics
Open Access Information
Publication FEEs
APCs: 2752 (GBP)
This journal publishes articles under the following open access license
OA License: CC BYCC BY-NC
Peer-Review
Single-blind Peer Review
Author's Copyright Notice
Author's Rights:--
Journal Metrics

Impact Factor

Journal Impact Factor(JIF) 2024
杚.炆
Journal Citation Indicator(JCI) 2024
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Web of Science Rank 2024:
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# 䟕/声炆篫
94.91%
Q1

CiteScore Metrics

CiteScore 2024
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  声逦躭鋺 Citation (2022-2024)
  缗篫鋺 Article (2022-2024)
CiteScore Rank 2024
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# 缗鋺/缗䟕杚
91.81%
Q1
偌锆囹锆嚷详锆嚷桧详, 鵝㥌肃懼囹懼嚷⻊嚷锆续 囹鵃罒 侶鵃桧蠹㥌锆囹嚷鵃锆续
# 缗缗/声篫逦
87.43%
Q1
Abstracting Indexing