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Journal of Computational and Graphical Statistics is a peer-reviewed journal published by Taylor & Francis, covering Statistical Methods and Inference, Bayesian Methods and Mixture Models, Statistical Methods and Bayesian Inference. Over its lifetime it has published 2,751 papers, which have received 136,573 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." Journal of Computational and Graphical Statistics 12 (3): 45–58.
Formats any DOI in Journal of Computational and Graphical Statistics style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Bayesian Methods and Mixture Models Statistical Methods and Bayesian Inference Advanced Statistical Methods and Models Markov Chains and Monte Carlo Methods |
| ISSN | 1061-8600 |
| Citation impact (2-yr) | 1.22 |
| h-index | 146 |
| i10-index | 1,318 |
| Top institutions publishing here | University of Washington |
| 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.
Abstract In this article we discuss our experience designing and implementing a statistical computing language. In developing this new language, we sought to combine what we felt were useful features from two existing computer languages. We feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scoping.
Abstract We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the…
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of exhaustive search procedures usually applied to fit such models have been known for a long time: overfitting and a selection bias towards covariates with many possible splits or missing values. While pruning procedures are able to solve the overfitting problem, the…
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso…
