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Robust principal components for hyperspectral data analysis
(Springer, 2009-07)
Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the ...
Diagnosis of a battery energy storage system based on principal component analysis
This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, ...
Conformational analysis: A new approach by means of chemometrics
(Wiley-blackwellMaldenEUA, 2002)
Robust estimators under a functional common principal components model
(Elsevier Science, 2017-09)
When dealing with several populations of functional data, equality of the covariance operators is often assumed even when seeking for a lower-dimensional approximation to the data. Usually, if this assumption does not hold, ...
Detecting influential observations in principal components and common principal components
(Elsevier Science, 2010-12)
Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal ...
Principal Component Analysis For Reservoir Uncertainty Reduction
(Springer HeidelbergHeidelberg, 2016)
PRINCIPAL COMPONENT ANALYSIS OF THE C-13 NMR SHIFTS OF NORBORNYL DERIVATIVES .2. TETRACYCLIC DODECANE DERIVATIVES
(John Wiley & Sons LtdW SussexInglaterra, 1993)
Principal Component Analysis For Reservoir Uncertainty Reduction
(SPRINGER HEIDELBERGHEIDELBERG, 2016)