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|a 10.1080/02664763.2024.2346343
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|a eng
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|a Sun, Mingwei
|e verfasserin
|4 aut
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|a A principal-weighted penalized regression model and its application in economic modeling
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 08.11.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Informa UK Limited, trading as Taylor & Francis Group.
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|a This paper introduces a novel Principal-Weighted Penalized (PWP) regression model, designed for dimensionality reduction in large datasets without sacrificing essential information. This new model retains the favorable features of the principal component analysis (PCA) technique and penalized regression models. It weighs the variables in a large data set based on their contributions to principal components identified by PCA, enhancing its capacity to uncover crucial hidden variables. The PWP model also efficiently performs variable selection and estimates regression coefficients through regularization. An application of the proposed model on high-dimensional economic data is studied. The results of comparative studies in simulations and a real example in economic modeling demonstrate its superior fitting and predictive abilities. The resulting model excels in accuracy and interpretability, outperforming existing methods
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|a Journal Article
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|a Principal component analysis
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| 650 |
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|a dimension reduction
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| 650 |
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|a penalized regression
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| 650 |
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|a principal-weighted
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| 650 |
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|a variable selection
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| 700 |
1 |
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|a Xu, Murong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of applied statistics
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|g 51(2024), 15 vom: 06., Seite 3215-3232
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