Sparsity information and regularization in the horseshoe and other shrinkage priors
- Authors
- Juho PiironenAki Vehtari
- Journal
- Electronic Journal of Statistics
In The Last Decade
doi.org/10.1214/17-ejs1337si →Countries where authors are citing Sparsity information and regularization in the horseshoe and other shrinkage priors
This map shows the geographic impact of Sparsity information and regularization in the horseshoe and other shrinkage priors. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sparsity information and regularization in the horseshoe and other shrinkage priors with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sparsity information and regularization in the horseshoe and other shrinkage priors more than expected).
Fields of papers citing Sparsity information and regularization in the horseshoe and other shrinkage priors
This network shows the impact of Sparsity information and regularization in the horseshoe and other shrinkage priors. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Sparsity information and regularization in the horseshoe and other shrinkage priors.
About Sparsity information and regularization in the horseshoe and other shrinkage priors
This paper, published in 2017, received 250 indexed citations . Written by Juho Piironen and Aki Vehtari covering the research area of Statistics and Probability and Computational Mechanics. It is primarily cited by scholars working on Statistics and Probability (76 citations), Artificial Intelligence (56 citations) and Molecular Biology (32 citations). Published in Electronic Journal of Statistics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1214/17-ejs1337si.