| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 93 | 79 | 116 | 18 |
Journal of The Royal Statistical Society Series B-statistical Methodology primarily focuses on research topics in Statistics, Econometrics, Estimator, Applied mathematics and Algorithm. Regression analysis, Covariate, Nonparametric statistics, Likelihood function and Statistical hypothesis testing are all areas of Statistics tackled in the journal. In it, Linear regression and Regression are investigated in conjunction with one another to address concerns in Regression analysis research.
While Econometrics is the focus of Journal of The Royal Statistical Society Series B-statistical Methodology, it also provided insights into the studies of Inference, Missing data and Parametric statistics. It dives deep in exploring the relationship between the study of Estimator and Estimation theory. The research on Applied mathematics featured in the journal combines topics in other fields like Smoothing, Linear model, Mathematical optimization and Calculus.
The concepts on Algorithm presented in Journal of The Royal Statistical Society Series B-statistical Methodology can also apply to other research fields, including Monte Carlo method, Prior probability and Markov chain. The journal centers on topics in Monte Carlo method, with a focus on Markov chain Monte Carlo. Research on Prior probability addressed in it frequently intersections with the field of Posterior probability.
The journal papers are organized to address concerns in the fields of Statistics, Algorithm, Econometrics, Mathematical optimization and Applied mathematics. The most cited papers tackle studies in Monte Carlo method and the interrelated subject of Markov chain to gain insights into Algorithm. The most cited papers explore research in Likelihood function and overlapping concepts in Semiparametric regression and Restricted maximum likelihood to expand the discourse in Econometrics.
Estimator, Statistics, Algorithm, Inference and Covariate are among the topics commonly tackled in Journal of The Royal Statistical Society Series B-statistical Methodology. The research on Estimator tackled can also make contributions to studies in the areas of Quantile, Linear model, Instrumental variable and Least squares. Interdisciplinary research on topics like Statistics and False discovery rate are the foci of Journal of The Royal Statistical Society Series B-statistical Methodology.
Regularization (mathematics) are all disciplines of Algorithm that connect with topics in Node (circuits). While work presented in the journal provided substantial information on Inference, it also covered topics in Sample size determination, Statistical inference, Consistency (statistics), Randomized experiment and Sample (statistics). The journal explores topics in Covariate which can be helpful for research in disciplines like Mathematical optimization and Regression.
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 The Royal Statistical Society Series B-statistical Methodology (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 The Royal Statistical Society Series B-statistical Methodology (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, 8.16% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.11% were posted by at least one author from the top 10 institutions publishing in the journal. Another 13.33% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.22% of all publications and 33.33% 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.
Dominik Rothenhäusler;Nicolai Meinshausen;Peter Bühlmann;Jonas Peters
(2021)Sai Li;T. Tony Cai;Hongzhe Li
(2021)Xu Shi;Wang Miao;Jennifer C. Nelson;Eric J. Tchetgen Tchetgen
(2020)Lihua Lei;Emmanuel J. Candès
(2021)David T. Frazier;Christian P. Robert;Judith Rousseau
(2020)Jeremy Heng;Arnaud Doucet;Yvo Pokern
(2021)Holger Dette;Kevin Kokot;Stanislav Volgushev
(2020)Dong Xia;Ming Yuan
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