Computational Statistics and Data Analysis

Aims & Scope

Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis.

The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms.

Software and algorithms can be submitted with manuscripts and will be stored together with the online article.

II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.6

SJR (SCImago Journal Rank)

Year	Value
2024	0.885

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	138

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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 Computer Science 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.

• Stochastic gradient boosting

  Citation: 5328

  Authors: Jerome H.

• PLS path modeling

  Citation: 4491

  Authors: Michel, Vincenzo Esposito, Yves-Marie, Carlo

• Algorithms and applications for approximate nonnegative matrix factorization

  Citation: 1154

  Authors: Michael W., Murray, Amy N., V. Paul, Robert J.

• Empirical characterization of random forest variable importance measures

  Citation: 886

  Authors: Kellie J., Ryan V.

• Robust smoothing of gridded data in one and higher dimensions with missing values

  Citation: 838

  Authors: Damien

• Consistent and asymptotically normal PLS estimators for linear structural equations

  Citation: 784

  Authors: Theo K., Jörg

• Practical variable selection for generalized additive models

  Citation: 664

  Authors: Giampiero, Simon N.

• Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap

  Citation: 632

  Authors: Ji-Hyun

• How many principal components? stopping rules for determining the number of non-trivial axes revisited

  Citation: 616

  Authors: Pedro R., Donald A., Keith M.

• Testing and dating of structural changes in practice

  Citation: 604

  Authors: Achim, Christian, Walter, Kurt

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