A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity
Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is e...
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Gespeichert in:
| Autor*in: |
Wu, Zeyu [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Annals of the Institute of Statistical Mathematics - Springer Japan, 1949, 75(2022), 4 vom: 08. Dez., Seite 619-648 |
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| Übergeordnetes Werk: |
volume:75 ; year:2022 ; number:4 ; day:08 ; month:12 ; pages:619-648 |
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| DOI / URN: |
10.1007/s10463-022-00856-0 |
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OLC2143842627 |
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| 520 | |a Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. | ||
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| 700 | 1 | |a Liu, Weidong |4 aut | |
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10.1007/s10463-022-00856-0 doi (DE-627)OLC2143842627 (DE-He213)s10463-022-00856-0-p DE-627 ger DE-627 rakwb eng 510 VZ Wu, Zeyu verfasserin aut A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. Gaussian graphical model High-dimensional data Lasso Precision matrix Weak sparsity Wang, Cheng aut Liu, Weidong aut Enthalten in Annals of the Institute of Statistical Mathematics Springer Japan, 1949 75(2022), 4 vom: 08. Dez., Seite 619-648 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:75 year:2022 number:4 day:08 month:12 pages:619-648 https://doi.org/10.1007/s10463-022-00856-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2018 AR 75 2022 4 08 12 619-648 |
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10.1007/s10463-022-00856-0 doi (DE-627)OLC2143842627 (DE-He213)s10463-022-00856-0-p DE-627 ger DE-627 rakwb eng 510 VZ Wu, Zeyu verfasserin aut A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. Gaussian graphical model High-dimensional data Lasso Precision matrix Weak sparsity Wang, Cheng aut Liu, Weidong aut Enthalten in Annals of the Institute of Statistical Mathematics Springer Japan, 1949 75(2022), 4 vom: 08. Dez., Seite 619-648 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:75 year:2022 number:4 day:08 month:12 pages:619-648 https://doi.org/10.1007/s10463-022-00856-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2018 AR 75 2022 4 08 12 619-648 |
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10.1007/s10463-022-00856-0 doi (DE-627)OLC2143842627 (DE-He213)s10463-022-00856-0-p DE-627 ger DE-627 rakwb eng 510 VZ Wu, Zeyu verfasserin aut A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. Gaussian graphical model High-dimensional data Lasso Precision matrix Weak sparsity Wang, Cheng aut Liu, Weidong aut Enthalten in Annals of the Institute of Statistical Mathematics Springer Japan, 1949 75(2022), 4 vom: 08. Dez., Seite 619-648 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:75 year:2022 number:4 day:08 month:12 pages:619-648 https://doi.org/10.1007/s10463-022-00856-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2018 AR 75 2022 4 08 12 619-648 |
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10.1007/s10463-022-00856-0 doi (DE-627)OLC2143842627 (DE-He213)s10463-022-00856-0-p DE-627 ger DE-627 rakwb eng 510 VZ Wu, Zeyu verfasserin aut A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. Gaussian graphical model High-dimensional data Lasso Precision matrix Weak sparsity Wang, Cheng aut Liu, Weidong aut Enthalten in Annals of the Institute of Statistical Mathematics Springer Japan, 1949 75(2022), 4 vom: 08. Dez., Seite 619-648 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:75 year:2022 number:4 day:08 month:12 pages:619-648 https://doi.org/10.1007/s10463-022-00856-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2018 AR 75 2022 4 08 12 619-648 |
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10.1007/s10463-022-00856-0 doi (DE-627)OLC2143842627 (DE-He213)s10463-022-00856-0-p DE-627 ger DE-627 rakwb eng 510 VZ Wu, Zeyu verfasserin aut A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. Gaussian graphical model High-dimensional data Lasso Precision matrix Weak sparsity Wang, Cheng aut Liu, Weidong aut Enthalten in Annals of the Institute of Statistical Mathematics Springer Japan, 1949 75(2022), 4 vom: 08. Dez., Seite 619-648 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:75 year:2022 number:4 day:08 month:12 pages:619-648 https://doi.org/10.1007/s10463-022-00856-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2018 AR 75 2022 4 08 12 619-648 |
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Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently. © The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2143842627</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118092028.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10463-022-00856-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2143842627</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10463-022-00856-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Zeyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Institute of Statistical Mathematics, Tokyo 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we estimate the high-dimensional precision matrix under the weak sparsity condition where many entries are nearly zero. We revisit the sparse column-wise inverse operator estimator and derive its general error bounds under the weak sparsity condition. A unified framework is established to deal with various cases including the heavy-tailed data, the non-paranormal data, and the matrix variate data. These new methods can achieve the same convergence rates as the existing methods and can be implemented efficiently.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gaussian graphical model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-dimensional data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lasso</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Precision matrix</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weak sparsity</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Cheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Weidong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of the Institute of Statistical Mathematics</subfield><subfield code="d">Springer Japan, 1949</subfield><subfield code="g">75(2022), 4 vom: 08. 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