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Communications in Statistics - Simulation and Computation is a peer-reviewed journal published by Taylor & Francis, covering Advanced Statistical Methods and Models, Statistical Methods and Inference, Statistical Methods and Bayesian Inference. Over its lifetime it has published 7,319 papers, which have received 78,370 citations.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Communications in Statistics - Simulation and Computation 12 (3): 45–58.
Formats any DOI in Communications in Statistics - Simulation and Computation style. No sign-up. |
| Publishes research in | Advanced Statistical Methods and Models Statistical Methods and Inference Statistical Methods and Bayesian Inference Statistical Distribution Estimation and Applications Advanced Statistical Process Monitoring |
| ISSN | 0361-0918 |
| Citation impact (2-yr) | 0.8 |
| h-index | 89 |
| i10-index | 1,847 |
| Top institutions publishing here | McMaster University |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and…
The odds ratio (OR) is probably the most widely used index of effect size in epidemiological studies. The difficulty of interpreting the OR has troubled many clinical researchers and epidemiologists for a long time. We propose a new method for interpreting the size of the OR by relating it to differences in a normal standard…
A method for inducing a desired rank correlation matrix on a multivariate input random variable for use in a simulation study is introduced in this paper. This method is simple to use, is distribution free, preserves the exact form of the marginal distributions on the input variables, and may be used with any type of…
This article describes a unified approach to variance modeling and inference in the context of a general form of the normal-theory linear mixed model. The primary variance modeling objects are parameterized covari-ance structures, examples being diagonal, compound-symmetry, unstructured, timeseries, and spatial. These structures can enter in two different places in the general mixed model, and…
