Adaptive penalized M-estimation with current status data
Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum...
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Gespeichert in:
| Autor*in: |
Ma, Shuangge [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Anmerkung: |
© The Institute of Statistical Mathematics, Tokyo 2006 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of the Institute of Statistical Mathematics - Springer-Verlag, 1949, 58(2006), 3 vom: 11. Juli, Seite 511-526 |
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| Übergeordnetes Werk: |
volume:58 ; year:2006 ; number:3 ; day:11 ; month:07 ; pages:511-526 |
| Links: |
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| DOI / URN: |
10.1007/s10463-005-0026-4 |
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| Katalog-ID: |
OLC2071688767 |
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10.1007/s10463-005-0026-4 doi (DE-627)OLC2071688767 (DE-He213)s10463-005-0026-4-p DE-627 ger DE-627 rakwb eng 510 VZ Ma, Shuangge verfasserin aut Adaptive penalized M-estimation with current status data 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2006 Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. Adaptive semiparametric estimation Current status data Penalized M-estimator Kosorok, Michael R. aut Enthalten in Annals of the Institute of Statistical Mathematics Springer-Verlag, 1949 58(2006), 3 vom: 11. Juli, Seite 511-526 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:58 year:2006 number:3 day:11 month:07 pages:511-526 https://doi.org/10.1007/s10463-005-0026-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_193 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4323 GBV_ILN_4324 AR 58 2006 3 11 07 511-526 |
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10.1007/s10463-005-0026-4 doi (DE-627)OLC2071688767 (DE-He213)s10463-005-0026-4-p DE-627 ger DE-627 rakwb eng 510 VZ Ma, Shuangge verfasserin aut Adaptive penalized M-estimation with current status data 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2006 Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. Adaptive semiparametric estimation Current status data Penalized M-estimator Kosorok, Michael R. aut Enthalten in Annals of the Institute of Statistical Mathematics Springer-Verlag, 1949 58(2006), 3 vom: 11. Juli, Seite 511-526 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:58 year:2006 number:3 day:11 month:07 pages:511-526 https://doi.org/10.1007/s10463-005-0026-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_193 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4323 GBV_ILN_4324 AR 58 2006 3 11 07 511-526 |
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10.1007/s10463-005-0026-4 doi (DE-627)OLC2071688767 (DE-He213)s10463-005-0026-4-p DE-627 ger DE-627 rakwb eng 510 VZ Ma, Shuangge verfasserin aut Adaptive penalized M-estimation with current status data 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2006 Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. Adaptive semiparametric estimation Current status data Penalized M-estimator Kosorok, Michael R. aut Enthalten in Annals of the Institute of Statistical Mathematics Springer-Verlag, 1949 58(2006), 3 vom: 11. Juli, Seite 511-526 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:58 year:2006 number:3 day:11 month:07 pages:511-526 https://doi.org/10.1007/s10463-005-0026-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_193 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4323 GBV_ILN_4324 AR 58 2006 3 11 07 511-526 |
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10.1007/s10463-005-0026-4 doi (DE-627)OLC2071688767 (DE-He213)s10463-005-0026-4-p DE-627 ger DE-627 rakwb eng 510 VZ Ma, Shuangge verfasserin aut Adaptive penalized M-estimation with current status data 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2006 Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. Adaptive semiparametric estimation Current status data Penalized M-estimator Kosorok, Michael R. aut Enthalten in Annals of the Institute of Statistical Mathematics Springer-Verlag, 1949 58(2006), 3 vom: 11. Juli, Seite 511-526 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:58 year:2006 number:3 day:11 month:07 pages:511-526 https://doi.org/10.1007/s10463-005-0026-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_193 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4323 GBV_ILN_4324 AR 58 2006 3 11 07 511-526 |
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10.1007/s10463-005-0026-4 doi (DE-627)OLC2071688767 (DE-He213)s10463-005-0026-4-p DE-627 ger DE-627 rakwb eng 510 VZ Ma, Shuangge verfasserin aut Adaptive penalized M-estimation with current status data 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Institute of Statistical Mathematics, Tokyo 2006 Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. Adaptive semiparametric estimation Current status data Penalized M-estimator Kosorok, Michael R. aut Enthalten in Annals of the Institute of Statistical Mathematics Springer-Verlag, 1949 58(2006), 3 vom: 11. Juli, Seite 511-526 (DE-627)129934658 (DE-600)390313-8 (DE-576)015492907 0020-3157 nnns volume:58 year:2006 number:3 day:11 month:07 pages:511-526 https://doi.org/10.1007/s10463-005-0026-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_193 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4323 GBV_ILN_4324 AR 58 2006 3 11 07 511-526 |
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Adaptive penalized M-estimation with current status data |
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Adaptive penalized M-estimation with current status data |
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Ma, Shuangge |
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2006 |
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Ma, Shuangge Kosorok, Michael R. |
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10.1007/s10463-005-0026-4 |
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510 |
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adaptive penalized m-estimation with current status data |
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Adaptive penalized M-estimation with current status data |
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Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. © The Institute of Statistical Mathematics, Tokyo 2006 |
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Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. © The Institute of Statistical Mathematics, Tokyo 2006 |
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Abstract Current status data arises when a continuous response is reduced to an indicator of whether the response is greater or less than a random threshold value. In this article we consider adaptive penalized M-estimators (including the penalized least squares estimators and the penalized maximum likelihood estimators) for nonparametric and semiparametric models with current status data, under the assumption that the unknown nonparametric parameters belong to unknown Sobolev spaces. The Cox model is used as a representative of the semiparametric models. It is shown that the modified penalized M-estimators of the nonparametric parameters can achieve adaptive convergence rates, even when the degrees of smoothing are not known in advance. $$\sqrt{n}$$ consistency, asymptotic normality and inference based on the weighted bootstrap for the estimators of the regression parameter in the Cox model are also established. A simulation study is conducted for the Cox model to evaluate the finite sample efficacy of the proposed approach and to compare it with the ordinary maximum likelihood estimator. It is demonstrated that the proposed method is computationally superior.We apply the proposed approach to the California Partner Study analysis. © The Institute of Statistical Mathematics, Tokyo 2006 |
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Adaptive penalized M-estimation with current status data |
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