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Assessment of maximum likelihood PCA missing data imputation

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Assessment of maximum likelihood PCA missing data imputation

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Folch Fortuny, A.; Arteaga Moreno, FJ.; Ferrer, A. (2016). Assessment of maximum likelihood PCA missing data imputation. Journal of Chemometrics. 30(7):386-393. doi:10.1002/cem.2804

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/81809

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Title: Assessment of maximum likelihood PCA missing data imputation
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
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Abstract:
Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the ...[+]
Subjects: Maximum likelihood principal component analysis , missing data , regression-based methods , PCA model building , trimmed scores regression
Copyrigths: Reserva de todos los derechos
Source:
Journal of Chemometrics. (issn: 0886-9383 ) (eissn: 1099-128X )
DOI: 10.1002/cem.2804
Publisher:
Wiley
Publisher version: http://doi.org/10.1002/cem.2804
Thanks:
Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02 and DPI2014-55276-C5-1R, and the Spanish Ministry ...[+]
Type: Artículo

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