Estimation and inference on central mean subspace for multivariate response data
In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Liping [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
16 |
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| Übergeordnetes Werk: |
Enthalten in: An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center - Phillips, Eileen ELSEVIER, 2014, Amsterdam |
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| Übergeordnetes Werk: |
volume:92 ; year:2015 ; pages:68-83 ; extent:16 |
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| DOI / URN: |
10.1016/j.csda.2015.05.006 |
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| 520 | |a In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. | ||
| 520 | |a In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. | ||
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| 650 | 7 | |a Profile least squares |2 Elsevier | |
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10.1016/j.csda.2015.05.006 doi GBVA2015005000015.pica (DE-627)ELV039641244 (ELSEVIER)S0167-9473(15)00123-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Zhu, Liping verfasserin aut Estimation and inference on central mean subspace for multivariate response data 2015transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. Central mean subspace Elsevier Semiparametric efficiency Elsevier Sufficient dimension reduction Elsevier Multivariate response Elsevier Profile least squares Elsevier Zhong, Wei oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:92 year:2015 pages:68-83 extent:16 https://doi.org/10.1016/j.csda.2015.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 92 2015 68-83 16 045F 004 |
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10.1016/j.csda.2015.05.006 doi GBVA2015005000015.pica (DE-627)ELV039641244 (ELSEVIER)S0167-9473(15)00123-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Zhu, Liping verfasserin aut Estimation and inference on central mean subspace for multivariate response data 2015transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. Central mean subspace Elsevier Semiparametric efficiency Elsevier Sufficient dimension reduction Elsevier Multivariate response Elsevier Profile least squares Elsevier Zhong, Wei oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:92 year:2015 pages:68-83 extent:16 https://doi.org/10.1016/j.csda.2015.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 92 2015 68-83 16 045F 004 |
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10.1016/j.csda.2015.05.006 doi GBVA2015005000015.pica (DE-627)ELV039641244 (ELSEVIER)S0167-9473(15)00123-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Zhu, Liping verfasserin aut Estimation and inference on central mean subspace for multivariate response data 2015transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. Central mean subspace Elsevier Semiparametric efficiency Elsevier Sufficient dimension reduction Elsevier Multivariate response Elsevier Profile least squares Elsevier Zhong, Wei oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:92 year:2015 pages:68-83 extent:16 https://doi.org/10.1016/j.csda.2015.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 92 2015 68-83 16 045F 004 |
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10.1016/j.csda.2015.05.006 doi GBVA2015005000015.pica (DE-627)ELV039641244 (ELSEVIER)S0167-9473(15)00123-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Zhu, Liping verfasserin aut Estimation and inference on central mean subspace for multivariate response data 2015transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. Central mean subspace Elsevier Semiparametric efficiency Elsevier Sufficient dimension reduction Elsevier Multivariate response Elsevier Profile least squares Elsevier Zhong, Wei oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:92 year:2015 pages:68-83 extent:16 https://doi.org/10.1016/j.csda.2015.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 92 2015 68-83 16 045F 004 |
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Estimation and inference on central mean subspace for multivariate response data |
| abstract |
In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. |
| abstractGer |
In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. |
| abstract_unstemmed |
In this paper, we introduce the notion of the central mean subspace when the response is multivariate, and propose a profile least squares approach to perform estimation and inference. Unlike existing methods in the sufficient dimension reduction literature, the profile least squares method does not require any distributional assumptions on the covariates, and facilitates statistical inference on the central mean subspace. We demonstrate theoretically and empirically that the properly weighted profile least squares approach is more efficient than its unweighted counterpart. We further confirm the promising finite-sample performance of our proposal through comprehensive simulations and an application to an etiologic study on essential hypertension conducted in P. R. China. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 |
| title_short |
Estimation and inference on central mean subspace for multivariate response data |
| url |
https://doi.org/10.1016/j.csda.2015.05.006 |
| remote_bool |
true |
| author2 |
Zhong, Wei |
| author2Str |
Zhong, Wei |
| ppnlink |
ELV022563539 |
| mediatype_str_mv |
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| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.csda.2015.05.006 |
| up_date |
2024-07-06T21:07:32.993Z |
| _version_ |
1803865348368236544 |
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|
| score |
7.400529 |