Robust forecasting with scaled independent component analysis
Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shu, Lei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease - Dowman, Leona ELSEVIER, 2016, New York |
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| Übergeordnetes Werk: |
volume:51 ; year:2023 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.frl.2022.103399 |
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ELV059658800 |
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| 520 | |a Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. | ||
| 520 | |a Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. | ||
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10.1016/j.frl.2022.103399 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001995.pica (DE-627)ELV059658800 (ELSEVIER)S1544-6123(22)00576-1 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.90 bkl Shu, Lei verfasserin aut Robust forecasting with scaled independent component analysis 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis Elsevier Latent factor Elsevier Robust forecasting Elsevier High-dimension Elsevier Lu, Feiyang oth Chen, Yu oth Enthalten in Elsevier Science Dowman, Leona ELSEVIER Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease 2016 New York (DE-627)ELV014710919 volume:51 year:2023 pages:0 https://doi.org/10.1016/j.frl.2022.103399 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.90 Neurologie VZ AR 51 2023 0 |
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10.1016/j.frl.2022.103399 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001995.pica (DE-627)ELV059658800 (ELSEVIER)S1544-6123(22)00576-1 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.90 bkl Shu, Lei verfasserin aut Robust forecasting with scaled independent component analysis 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis Elsevier Latent factor Elsevier Robust forecasting Elsevier High-dimension Elsevier Lu, Feiyang oth Chen, Yu oth Enthalten in Elsevier Science Dowman, Leona ELSEVIER Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease 2016 New York (DE-627)ELV014710919 volume:51 year:2023 pages:0 https://doi.org/10.1016/j.frl.2022.103399 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.90 Neurologie VZ AR 51 2023 0 |
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10.1016/j.frl.2022.103399 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001995.pica (DE-627)ELV059658800 (ELSEVIER)S1544-6123(22)00576-1 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.90 bkl Shu, Lei verfasserin aut Robust forecasting with scaled independent component analysis 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis Elsevier Latent factor Elsevier Robust forecasting Elsevier High-dimension Elsevier Lu, Feiyang oth Chen, Yu oth Enthalten in Elsevier Science Dowman, Leona ELSEVIER Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease 2016 New York (DE-627)ELV014710919 volume:51 year:2023 pages:0 https://doi.org/10.1016/j.frl.2022.103399 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.90 Neurologie VZ AR 51 2023 0 |
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10.1016/j.frl.2022.103399 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001995.pica (DE-627)ELV059658800 (ELSEVIER)S1544-6123(22)00576-1 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.90 bkl Shu, Lei verfasserin aut Robust forecasting with scaled independent component analysis 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. Independent component analysis Elsevier Latent factor Elsevier Robust forecasting Elsevier High-dimension Elsevier Lu, Feiyang oth Chen, Yu oth Enthalten in Elsevier Science Dowman, Leona ELSEVIER Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease 2016 New York (DE-627)ELV014710919 volume:51 year:2023 pages:0 https://doi.org/10.1016/j.frl.2022.103399 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.90 Neurologie VZ AR 51 2023 0 |
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Shu, Lei |
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Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease |
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Reliability of the hand held dynamometer in measuring muscle strength in people with interstitial lung disease |
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Shu, Lei |
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10.1016/j.frl.2022.103399 |
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610 |
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robust forecasting with scaled independent component analysis |
| title_auth |
Robust forecasting with scaled independent component analysis |
| abstract |
Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. |
| abstractGer |
Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. |
| abstract_unstemmed |
Independent component analysis (ICA) is a method to find potential components from high-dimensional data. In this paper, a scaled independent component analysis (sICA) method is proposed for finding factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the independent components. Specifically, a one-dimensional linear regression is constructed for each covariate and target variable, where the regression coefficient measures the magnitude of the effect of the predictor variable on the target variable, and we use this regression coefficient to scale the corresponding predictor variable. Finally, we apply our method to study the data from the Federal Reserve Monthly Database for Economic Research (FRED-MD) which is a large macroeconomic database. The results of the data analysis show that, in general, the sICA method has better forecasting performance. |
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| title_short |
Robust forecasting with scaled independent component analysis |
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https://doi.org/10.1016/j.frl.2022.103399 |
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| author2 |
Lu, Feiyang Chen, Yu |
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Lu, Feiyang Chen, Yu |
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