Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93309
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Wang, C | en_US |
dc.creator | Jiang, B | en_US |
dc.date.accessioned | 2022-06-15T03:42:42Z | - |
dc.date.available | 2022-06-15T03:42:42Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/93309 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2019 Elsevier B.V. All rights reserved. | en_US |
dc.rights | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Wang, C., & Jiang, B. (2020). An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss. Computational Statistics & Data Analysis, 142, 106812 is available at https://doi.org/10.1016/j.csda.2019.106812 | en_US |
dc.subject | ADMM | en_US |
dc.subject | High dimension | en_US |
dc.subject | Penalized quadratic loss | en_US |
dc.subject | Precision matrix | en_US |
dc.title | An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 142 | en_US |
dc.identifier.doi | 10.1016/j.csda.2019.106812 | en_US |
dcterms.abstract | The estimation of high dimensional precision matrices has been a central topic in statistical learning. However, as the number of parameters scales quadratically with the dimension p, many state-of-the-art methods do not scale well to solve problems with a very large p. In this paper, we propose a very efficient algorithm for precision matrix estimation via penalized quadratic loss functions. Under the high dimension low sample size setting, the computation complexity of our algorithm is linear in both the sample size and the number of parameters. Such a computation complexity is in some sense optimal, as it is the same as the complexity needed for computing the sample covariance matrix. Numerical studies show that our algorithm is much more efficient than other state-of-the-art methods when the dimension p is very large. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computational statistics and data analysis, Feb. 2020, v. 142, 106812 | en_US |
dcterms.isPartOf | Computational statistics and data analysis | en_US |
dcterms.issued | 2020-02 | - |
dc.identifier.scopus | 2-s2.0-85069746604 | - |
dc.identifier.eissn | 0167-9473 | en_US |
dc.identifier.artn | 106812 | en_US |
dc.description.validate | 202206 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0205 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 23633121 | - |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Jiang_Efficient_Admm_Algorithm.pdf | Pre-Published version | 966.64 kB | Adobe PDF | View/Open |
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