Computational Statistics & Data Analysis

148.0k citations
6.1k papers · · active since 1950

Impact in

    • Statistical Methods and Inference
    • Statistical Methods and Bayesian Inference
    • Advanced Statistical Methods and Models
    • Statistical Distribution Estimation and Applications
    • Probabilistic and Robust Engineering Design
    • Advanced Statistical Process Monitoring

Papers in

Computational Statistics & Data Analysis

5.8k papers receiving 135.0k citations

Peers

Computational Statistics & Data Analysis
Comparison fields: 5 of 247
  • Statistics and Probability 51.7k
  • Statistics, Probability and Uncertainty 15.4k
  • Management Science and Operations Research 15.0k
  • Computational Mathematics 712
  • Finance 11.8k
Replace Journal of Multivariate Analysis with:
Journal of Multivariate Analysis United States
Journal of the Royal Statistical Society Series C (Applied Statistics) United Kingdom
The American Statistician United States
Journal of the Royal Statistical Society Series B (Statistical Methodology) United States
American Mathematical Monthly United States
Biometrics United States
Psychological Bulletin United States
Applied Mathematics and Computation China
Journal of the Royal Statistical Society Series A (Statistics in Society) United Kingdom
IEEE Transactions on Automatic Control United States
Computational Statistics & Data Analysis relative to Journal of Multivariate Analysis United States Journal of Multivariate Analysis's profile →
Citations per field
00.5×1.5×2.4×
Journal of Multivariate Analysis · 1×
Citations per year

Countries where authors publish in Computational Statistics & Data Analysis

Since Specialization
Citations

This map shows the geographic impact of research published in Computational Statistics & Data Analysis. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Computational Statistics & Data Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computational Statistics & Data Analysis more than expected).

Fields of papers published in Computational Statistics & Data Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Computational Statistics & Data Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Computational Statistics & Data Analysis.

About Computational Statistics & Data Analysis

The 6.1k papers published in Computational Statistics & Data Analysis in the last decades have received a total of 148.0k indexed citations . Papers published in Computational Statistics & Data Analysis usually cover Statistics and Probability (4.0k papers), Statistics, Probability and Uncertainty (786 papers), Management Science and Operations Research (876 papers), Finance (582 papers) and Computational Mathematics (34 papers) specifically the topics of Statistical Methods and Inference (2.0k papers), Statistical Methods and Bayesian Inference (1.6k papers), Advanced Statistical Methods and Models (1.6k papers), Bayesian Methods and Mixture Models (1.1k papers), Statistical Distribution Estimation and Applications (777 papers), Financial Risk and Volatility Modeling (552 papers), Optimal Experimental Design Methods (551 papers) and Statistical Methods in Clinical Trials (436 papers). The most active scholars publishing in Computational Statistics & Data Analysis are Jerome H. Friedman, Walter Krämer, Vincenzo Esposito Vinzi, Michel Tenenhaus, Carlo di Lauro, Clemens Tilke, Debasis Kundu, Damien Garcia, N. Balakrishnan and Uwe Ligges.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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