0167-9473
Published by: Elsevier
https://www.journals.elsevier.com/computational-statistics-and-data-analysis
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 150 | 68 | 119 | 14 |
| Computer Science | 628 | 16 | 22 | 9 |
Computational Statistics & Data Analysis is organized to address concerns in the fields of Statistics, Applied mathematics, Algorithm, Econometrics and Estimator. It concentrates on Statistics topics that focus on Regression analysis, Statistical hypothesis testing, Sample size determination, Covariate and Monte Carlo method. The research on Regression analysis featured in Computational Statistics & Data Analysis combines topics in other fields like Linear regression and Regression.
Computational Statistics & Data Analysis centers on topics in Statistical hypothesis testing, with a focus on Test statistic. The studies in Applied mathematics featured incorporate elements of Estimation theory, Linear model, Mathematical optimization and Calculus. Computational Statistics & Data Analysis explores topics in Algorithm which can be helpful for research in disciplines like Expectation–maximization algorithm, Cluster analysis, Artificial intelligence and Markov chain Monte Carlo.
Artificial intelligence research is the primary subject tackled in it with a focus on Linear discriminant analysis. The work on Econometrics addressed in it expands to the thematically related Bayesian probability. The research on Estimator tackled can also make contributions to studies in the areas of Mean squared error and Nonparametric statistics.
The journal articles tackle a plethora of topics, such as Statistics, Algorithm, Applied mathematics, Econometrics and Estimator. The study of Algorithm in the journal papers encompasses disciplines such as Cluster analysis, as well as fields such as Data mining, all of which overlap with one another. The journal articles address concerns in Econometrics which are intertwined with other disciplines, such as Statistical hypothesis testing, Parametric statistics and Markov chain Monte Carlo.
Computational Statistics & Data Analysis investigates studies in Algorithm, Covariate, Applied mathematics, Estimator and Model selection. While Algorithm is the focus of it, it also provided insights into the studies of Sampling (statistics), Quantile, Sliced inverse regression and Confidence interval. Computational Statistics & Data Analysis tackles topics on Covariate, which can potentially contribute to the wider field of Statistics.
The studies on Applied mathematics discussed can also contribute to research in the domains of Semiparametric model, Statistical inference, Prior probability and Principal component analysis. While work presented in it provided substantial information on Estimator, it also covered topics in Kernel (statistics), Distribution (mathematics), Set (abstract data type), Simple (abstract algebra) and B-spline. The work on Model selection tackled in the journal brings together disciplines like Data mining, Mathematical optimization and Generalized linear mixed model.
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 & Data Analysis (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 & Data Analysis (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 2022 edition, 16.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 4.76% were posted by at least one author from the top 10 institutions publishing in the journal. Another 23.81% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.05% of all publications and 52.38% 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.
Andrea Bommert;Xudong Sun;Bernd Bischl;Jörg Rahnenführer
(2020)Wenlin Dai;Tomas Mrkvicka;Ying Sun;Marc G. Genton
(2020)Karla Monterrubio-Gomez;Lassi Roininen;Sara K Wade;Theodoros Damoulas;Theodoros Damoulas
(2020)Yuna Zhao;Dennis K.J. Lin;Min-Qian Liu
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