Journal of the Royal Statistical Society. Series B: Statistical Methodology

Aims & Scope

Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly.

The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims.

The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably 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 (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science.

Reviews of methodological techniques are also considered.

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.6
2024	3.10

SJR (SCImago Journal Rank)

Year	Value
2024	3.308

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	159

Journal Rank

Year	Value
2024	604

Journal Citation Indicator

Year	Value
2024	881

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

  Citation: 83732

  Authors: Yoav, Yosef

• Regression Shrinkage and Selection Via the Lasso

  Citation: 35967

  Authors: Robert

• Regression Models and Life-Tables

  Citation: 35038

  Authors: D. R.

• Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

  Citation: 32114

  Authors: A. P., N. M., D. B.

• Regularization and Variable Selection Via the Elastic Net

  Citation: 14668

  Authors: Hui, Trevor

• An Analysis of Transformations

  Citation: 10654

  Authors: G. E. P., D. R.

• Bayesian Measures of Model Complexity and Fit

  Citation: 9688

  Authors: David J., Nicola G., Bradley P., Angelika

• Cross-Validatory Choice and Assessment of Statistical Predictions

  Citation: 7427

  Authors: M.

• Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models

  Citation: 5776

  Authors: Simon N.

• Model Selection and Estimation in Regression with Grouped Variables

  Citation: 4955

  Authors: Ming, Yi

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