| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 328 | 35 | 44 | 8 |
| Computer Science | 829 | 15 | 21 | 5 |
Computational Statistics tackles a plethora of topics, such as Statistics, Applied mathematics, Algorithm, Estimator and Mathematical optimization. The research on Statistics discussed in the journal draws on the closely related field of Econometrics. Topics in Algorithm were tackled in line with various other fields like Inference, Artificial intelligence and Expectation–maximization algorithm.
The work on Artificial intelligence addressed in the journal expands to the thematically related Data mining. Computational Statistics explores research in Estimator and the adjacent study of Mean squared error. The journal focuses on Mathematical optimization research which is adjacent to topics in Smoothing.
It encompasses presentations on Bayesian probability, specifically Markov chain Monte Carlo, Bayesian inference and Prior probability.
The most cited articles focus on Algorithm, Statistics, Artificial intelligence, Mathematical optimization and Estimator. The journal articles deal with Statistics in conjunction with Applied mathematics and similar fields in Multivariate statistics. While Artificial intelligence is the focus of the journal papers, it also provides insights into the studies of Machine learning and Pattern recognition.
Computational Statistics focuses largely on the fields of Statistics, Algorithm, Applied mathematics, Estimator and Bayesian probability. Computational Statistics facilitated presentations on Statistics research, particularly Covariate, Statistical hypothesis testing, Multivariate statistics, Sample size determination and Regression analysis. Computational Statistics addresses concerns in the field of Algorithm by exploring it in line with topics in Function (mathematics) which intersect with Expectation–maximization algorithm subjects.
In addition to Applied mathematics research, it aims to explore topics under Monte Carlo method, Markov chain Monte Carlo, Poisson distribution, Distribution (mathematics) and Statistical inference. It mainly concentrates on Estimator but also investigates its connection with concepts in disciplines such as
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Computational Statistics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Computational Statistics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 6.47% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 6.92% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.03% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.58% of all publications and 75.47% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Emrah Altun;Gauss M. Cordeiro
(2020)Paul-Christian Bürkner;Jonah Gabry;Aki Vehtari
(2021)Claude Renaux;Laura Buzdugan;Markus Kalisch;Peter Bühlmann
(2020)Cédric Beaulac;Jeffrey S. Rosenthal
(2020)Cathy W. S. Chen;Sangyeol Lee;Khemmanant Khamthong
(2021)Chaoran Hu;Vladimir Pozdnyakov;Jun Yan
(2020)Jeffrey S. Rosenthal;Aki Dote;Aki Dote;Keivan Dabiri;Hirotaka Tamura
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