Regularized estimation of large covariance matrices

730 indexed citations
published 2008

Countries where authors are citing Regularized estimation of large covariance matrices

Specialization
Citations

This map shows the geographic impact of Regularized estimation of large covariance matrices. 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 Regularized estimation of large covariance matrices with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Regularized estimation of large covariance matrices more than expected).

Fields of papers citing Regularized estimation of large covariance matrices

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Regularized estimation of large covariance matrices. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Regularized estimation of large covariance matrices.

About Regularized estimation of large covariance matrices

This paper, published in 2008, received 730 indexed citations . Written by Peter J. Bickel and Elizaveta Levina covering the research area of Statistics and Probability. It is primarily cited by scholars working on Statistics and Probability (461 citations), Artificial Intelligence (213 citations) and Computational Mechanics (122 citations).

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/w19460132.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026