An exponential inequality and its application to M estimators in multiple linear models
Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Deng, Xin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Statistical papers - Springer Berlin Heidelberg, 1988, 61(2018), 4 vom: 17. März, Seite 1607-1627 |
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| Übergeordnetes Werk: |
volume:61 ; year:2018 ; number:4 ; day:17 ; month:03 ; pages:1607-1627 |
| Links: |
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| DOI / URN: |
10.1007/s00362-018-0994-0 |
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| Katalog-ID: |
OLC2118541600 |
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10.1007/s00362-018-0994-0 doi (DE-627)OLC2118541600 (DE-He213)s00362-018-0994-0-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Deng, Xin verfasserin aut An exponential inequality and its application to M estimators in multiple linear models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive simulation studies to demonstrate the validity of obtained theoretical results. Exponential inequality Strong linear representation estimator Linear regression model Widely orthant dependent random variables Wang, Xuejun aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 61(2018), 4 vom: 17. März, Seite 1607-1627 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:61 year:2018 number:4 day:17 month:03 pages:1607-1627 https://doi.org/10.1007/s00362-018-0994-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_26 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4319 GBV_ILN_4326 AR 61 2018 4 17 03 1607-1627 |
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10.1007/s00362-018-0994-0 doi (DE-627)OLC2118541600 (DE-He213)s00362-018-0994-0-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Deng, Xin verfasserin aut An exponential inequality and its application to M estimators in multiple linear models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive simulation studies to demonstrate the validity of obtained theoretical results. Exponential inequality Strong linear representation estimator Linear regression model Widely orthant dependent random variables Wang, Xuejun aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 61(2018), 4 vom: 17. März, Seite 1607-1627 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:61 year:2018 number:4 day:17 month:03 pages:1607-1627 https://doi.org/10.1007/s00362-018-0994-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_26 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4319 GBV_ILN_4326 AR 61 2018 4 17 03 1607-1627 |
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Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive simulation studies to demonstrate the validity of obtained theoretical results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive simulation studies to demonstrate the validity of obtained theoretical results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive simulation studies to demonstrate the validity of obtained theoretical results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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