| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 535 | 9 | 9 | 5 |
Artificial intelligence, Statistics, Machine learning, Data mining and Econometrics are the subjects of interest in the journal. While work presented in it provided substantial information on Artificial intelligence, it also covered topics in Computational statistics and Pattern recognition. The presentations discussing Statistics offer insights in topics such as Nonparametric statistics and Regression.
Bayesian probability research presented is mostly focused on the subject of Markov chain Monte Carlo.
The most cited papers primarily focus on research topics in Artificial intelligence, Machine learning, Data mining, Algorithm and Bayesian probability. The published articles explore topics in Artificial intelligence which can be helpful for research in disciplines like Regression analysis and Pattern recognition. The journal articles hold forums on Pattern recognition that merge themes from other disciplines such as Latent variable model and Statistics, Partial least squares regression.
The journal tackles a plethora of topics, such as Applied mathematics, Artificial intelligence, Statistics, Dimensionality reduction and Cluster analysis. The Applied mathematics study presented in the journal encompasses related topics like Generalized linear model and Hierarchical generalized linear model and also examines its connection to subjects such as Flow field and Bayesian clustering. It addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Machine learning and Pattern recognition.
Pattern recognition research presented in it encompasses a variety of subjects, including Kalman filter, Cluster (physics) and Fuzzy clustering. It holds forums on Dimensionality reduction that merges themes from other disciplines such as Regression and Functional data analysis. While Wiley Interdisciplinary Reviews: Computational Statistics focused on Cluster analysis, it was also able to explore topics like Computational biology and Complex network.
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 Wiley Interdisciplinary Reviews: 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 Wiley Interdisciplinary Reviews: 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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.22% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.87% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.22% of all publications and 58.70% 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.
Emilio Porcu;Reinhard Furrer;Douglas W Nychka
(2021)Gerhard Tutz
(2021)Peter Filzmoser;Klaus Nordhausen
(2021)Nengxiang Ling;Philippe Vieu
(2021)Nicholas Polson;Vadim Sokolov
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