| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 74 | 71 | 115 | 20 |
| Computer Science | 427 | 37 | 50 | 14 |
| Engineering and Technology | 715 | 22 | 35 | 12 |
Algorithm, Mathematical optimization, Artificial intelligence, Statistics and Applied mathematics are among the topics commonly tackled in Journal of Computational and Graphical Statistics. Some problems in Algorithm that were presented in Journal of Computational and Graphical Statistics overlapped with concepts under Expectation–maximization algorithm, Lasso (statistics), Inference and Markov chain Monte Carlo. It holds forums on Markov chain Monte Carlo that merges themes from other disciplines such as Bayesian inference, Markov chain and Gibbs sampling.
Smoothing, Convergence (routing), Estimator, Nonparametric regression and Monte Carlo method are some topics wherein Mathematical optimization research discussed in Journal of Computational and Graphical Statistics have an impact. Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Machine learning, Data mining and Pattern recognition. The journal focuses on Statistics research which is adjacent to topics in Econometrics.
The most cited publications are mainly concerned with subjects like Mathematical optimization, Algorithm, Artificial intelligence, Applied mathematics and Statistics. While Algorithm is the key highlight in the published articles, thet also covered some subjects on Expectation–maximization algorithm and Likelihood function. The journal publications hold forums on Artificial intelligence that merge themes from other disciplines such as Machine learning and Pattern recognition.
The main research concerns discussed in the journal are Algorithm, Markov chain Monte Carlo, Artificial intelligence, Statistics and Bayesian probability. Issues in Algorithm were discussed, taking into consideration concepts from other disciplines like Sampling (statistics), Lasso (statistics), Approximate Bayesian computation and Bayesian inference. It focuses on Markov chain Monte Carlo but the discussions also offer insight into other areas such as Stochastic process, Statistical physics, Importance sampling and Gibbs sampling.
The work on Artificial intelligence tackled in Journal of Computational and Graphical Statistics brings together disciplines like Uncertainty quantification, Machine learning and Pattern recognition. Statistics research in it involves the investigation of Inference studies, all of which are linked to disciplines such as Expectation–maximization algorithm and Random forest. The study of Regression and how it intertwines with concepts under Mathematical optimization were explored in the presented Regression analysis research.
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 Journal of Computational and Graphical 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 Journal of Computational and Graphical 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, 1.60% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.95% were posted by at least one author from the top 10 institutions publishing in the journal. Another 13.82% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 25.20% of all publications and 39.02% 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.
Matteo Fasiolo;Raphaël Nedellec;Yannig Goude;Simon N. Wood
(2020)M.-N. Tran;N. Nguyen;D. Nott;R. Kohn
(2020)M. T. Pratola;H. A. Chipman;E. I. George;R. E. McCulloch
(2020)Priyanga Dilini Talagala;Rob J. Hyndman;Kate Smith-Miles;Sevvandi Kandanaarachchi
(2020)Priyanga Dilini Talagala;Priyanga Dilini Talagala;Priyanga Dilini Talagala;Rob J. Hyndman;Rob J. Hyndman;Kate Smith-Miles
(2021)Bradley Efron;Balasubramanian Narasimhan
(2020)Cencheng Shen;Joshua T. Vogelstein
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