| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 755 | 5 | 6 | 2 |
The journal is mainly concerned with subjects like Statistics, Econometrics, Estimator, Applied mathematics and Artificial intelligence. Mean squared error, Test statistic, Statistic, Sample size determination and Monte Carlo method are all topics related to Statistics research discussed. Estimator research presented is mostly focused on the subject of Minimum-variance unbiased estimator.
It features Applied mathematics research that overlaps with concepts in Mathematical optimization. Artificial intelligence research featured in Communications for Statistical Applications and Methods incorporates concerns from various other topics such as Machine learning and Pattern recognition.
The journal articles primarily tackle Statistics, Applied mathematics, Estimator, Mean squared error and Estimation theory. The journal papers focus on Applied mathematics but the discussions also offer insight into other areas such as Dependent Dirichlet process, Nonparametric bayesian methods, Dirichlet process, Markov chain Monte Carlo and Mixed model. Issues in Estimator were discussed in the published articles, taking into consideration concepts from other disciplines like Censoring (clinical trials) and Bayes' theorem.
The aim of Communications for Statistical Applications and Methods is to expand the discussion of research in Statistics, Regression, Applied mathematics, Estimator and Artificial intelligence. It focuses on Statistics but the discussions also offer insight into other areas such as Treatment strategy and Stage (hydrology). It covers research in Regression, particularly Nonparametric regression and how they are related with concepts in Stock return.
While work presented in it provided substantial information on Applied mathematics, it also covered topics in Bayes estimator, Inverse, Linear regression and Bayes' theorem. It explores issues in Estimator which can be linked to other research areas like Skewness, Measure (mathematics), Association (psychology), Kendall s and Interval (graph theory). The concepts on Artificial intelligence presented in it can also apply to other research fields, including Machine learning and Pattern recognition.
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 Communications for Statistical Applications and Methods (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 Communications for Statistical Applications and Methods (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, 5.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 58.33% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.44% of all publications and 16.67% 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.
Dongwoo Kim;Young Kyung Lee;Byeong U. Park
(2020)Jae-Hwan Jung;Seong-Jin Ji;Hongtu Zhu;Joseph G. Ibrahim
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