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We also show that a correlation-based version of the method exhibits better rates in the operator norm. We also derive a fast iterative algorithm for computing the estimator, which relies on the popular Cholesky decomposition of the inverse but produces a permutation-invariant estimator. The method is compared to other estimators on simulated data and on a real data example of tumor tissue classification using gene expression data.<\/jats:p>","DOI":"10.1214\/08-ejs176","type":"journal-article","created":{"date-parts":[[2008,6,25]],"date-time":"2008-06-25T04:23:34Z","timestamp":1214367814000},"page":"494-515","source":"Crossref","is-referenced-by-count":534,"title":["Sparse permutation invariant covariance estimation"],"prefix":"10.1214","volume":"2","author":[{"given":"Adam J.","family":"Rothman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Bickel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizaveta","family":"Levina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"36","doi-asserted-by":"publisher","unstructured":"Yuan, M. and Lin, Y. 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