Generalized orthogonal components regression for high dimensional generalized linear models
The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Yanzhu [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: |
9 |
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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:88 ; year:2015 ; pages:119-127 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.csda.2015.02.006 |
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ELV039640671 |
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| 520 | |a The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. | ||
| 520 | |a The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. | ||
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| 700 | 1 | |a Zhang, Dabao |4 oth | |
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10.1016/j.csda.2015.02.006 doi GBVA2015005000015.pica (DE-627)ELV039640671 (ELSEVIER)S0167-9473(15)00045-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Lin, Yanzhu verfasserin aut Generalized orthogonal components regression for high dimensional generalized linear models 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. Categorical data Elsevier Collinear Elsevier Classification Elsevier Dimension reduction multicollinear Elsevier Zhang, Min oth Zhang, Dabao 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:88 year:2015 pages:119-127 extent:9 https://doi.org/10.1016/j.csda.2015.02.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 88 2015 119-127 9 045F 004 |
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10.1016/j.csda.2015.02.006 doi GBVA2015005000015.pica (DE-627)ELV039640671 (ELSEVIER)S0167-9473(15)00045-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Lin, Yanzhu verfasserin aut Generalized orthogonal components regression for high dimensional generalized linear models 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. Categorical data Elsevier Collinear Elsevier Classification Elsevier Dimension reduction multicollinear Elsevier Zhang, Min oth Zhang, Dabao 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:88 year:2015 pages:119-127 extent:9 https://doi.org/10.1016/j.csda.2015.02.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 88 2015 119-127 9 045F 004 |
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10.1016/j.csda.2015.02.006 doi GBVA2015005000015.pica (DE-627)ELV039640671 (ELSEVIER)S0167-9473(15)00045-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Lin, Yanzhu verfasserin aut Generalized orthogonal components regression for high dimensional generalized linear models 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. Categorical data Elsevier Collinear Elsevier Classification Elsevier Dimension reduction multicollinear Elsevier Zhang, Min oth Zhang, Dabao 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:88 year:2015 pages:119-127 extent:9 https://doi.org/10.1016/j.csda.2015.02.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 88 2015 119-127 9 045F 004 |
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10.1016/j.csda.2015.02.006 doi GBVA2015005000015.pica (DE-627)ELV039640671 (ELSEVIER)S0167-9473(15)00045-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Lin, Yanzhu verfasserin aut Generalized orthogonal components regression for high dimensional generalized linear models 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. Categorical data Elsevier Collinear Elsevier Classification Elsevier Dimension reduction multicollinear Elsevier Zhang, Min oth Zhang, Dabao 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:88 year:2015 pages:119-127 extent:9 https://doi.org/10.1016/j.csda.2015.02.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 88 2015 119-127 9 045F 004 |
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10.1016/j.csda.2015.02.006 doi GBVA2015005000015.pica (DE-627)ELV039640671 (ELSEVIER)S0167-9473(15)00045-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Lin, Yanzhu verfasserin aut Generalized orthogonal components regression for high dimensional generalized linear models 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. Categorical data Elsevier Collinear Elsevier Classification Elsevier Dimension reduction multicollinear Elsevier Zhang, Min oth Zhang, Dabao 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:88 year:2015 pages:119-127 extent:9 https://doi.org/10.1016/j.csda.2015.02.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 88 2015 119-127 9 045F 004 |
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Generalized orthogonal components regression for high dimensional generalized linear models |
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The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. |
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The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. |
| abstract_unstemmed |
The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a different set of components at each iteration and thus cannot provide a convergent set of components. Second, existing extensions are computationally intensive because of repetitively constructing a full set of components. Third, although they pursue the convergence of regression coefficients, the resultant regression coefficients may still diverge especially when building logistic regression models. GOCRE instead sequentially builds up each orthogonal component upon convergent construction, and simultaneously regresses against these orthogonal components to fit the GLM. The performance of the new method is demonstrated by both simulation studies and a real data example. |
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