Computational Statistics

Aims & Scope

Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics.

The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa.

The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing.

CompStat publishes hardware, software plus package reports.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.4

SJR (SCImago Journal Rank)

Year	Value
2024	0.750

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	55

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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.

• Goodness-of-fit indices for partial least squares path modeling

  Citation: 1159

  Authors: Jörg, Marko

• maxLik: A package for maximum likelihood estimation in R

  Citation: 445

  Authors: Arne, Ott

• Bayesian spatial modeling of genetic population structure

  Citation: 350

  Authors: Jukka, Jukka, Elja

• Adaptive proposal distribution for random walk Metropolis algorithm

  Citation: 319

  Authors: Heikki, Eero, Johanna

• What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?

  Citation: 288

  Authors: Bruce G., Anca M.

• Open-source machine learning: R meets Weka

  Citation: 278

  Authors: Kurt, Christian, Achim

• Type S error rates for classical and Bayesian single and multiple comparison procedures

  Citation: 276

  Authors: Andrew, Francis

• On the convergence of the partial least squares path modeling algorithm

  Citation: 250

  Authors: Jörg

• Robust estimation and classification for functional data via projection-based depth notions

  Citation: 208

  Authors: Antonio, Manuel, Ricardo

• Smoothing and mixed models

  Citation: 204

  Authors: M. P.

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