Results 11 to 20 of about 563,170 (163)
Modal Principal Component Analysis [PDF]
Neural Computation, 2020 Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Sando, Keishi, Hino, Hideitsu
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Principal Dynamical Components [PDF]
Communications on Pure and Applied Mathematics, 2012 AbstractA procedure is proposed for a dimension reduction in time series. Similarly to principal components, the procedure seeks a low‐dimensional manifold that minimizes information loss. Unlike principal components, however, the procedure involves dynamical considerations through the proposal of a predictive dynamical model in the reduced manifold ...
Domínguez de la Iglesia, Manuel +1 more
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Dynamic Functional Principal Components [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014 SummaryWe address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such functional time series frequently arise, for example, when a continuous time process is segmented into some smaller natural units, such as days. Then each X t represents one intraday curve.
Hörmann, Siegfried +2 more
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Stock price prediction using principal components.
PLoS ONE, 2020 The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set.
Mahsa Ghorbani, Edwin K P Chong
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Characteristics of Principal Components in Stock Price Correlation
Frontiers in Physics, 2021 The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix of assets returns.
Wataru Souma
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Interpretability of Composite Indicators Based on Principal Components
Journal of Probability and Statistics, 2022 Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and ...
Kris Boudt +3 more
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A new statistical method for the comparison of biplots with the same objects and variables
Ecological Indicators, 2023 Ordinations are compared most commonly by Procrustes methods applicable to points belonging to the same domain, either the objects or the variables describing them.
Attila István Engloner, János Podani
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Genetic associations between visual scores, body weight and age at first calving in nellore breed cattle [PDF]
Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2020 Body weight records of 231,416 Nellore females obtained from the Brazilian Association of Zebu Breeders were used to determine a linear combination between visual appraisal scores (body structure, precocity and muscling) using principal components ...
F.B. Freitas +5 more
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The classification of concentration of mixture of analytes
Lietuvos Matematikos Rinkinys, 2004 This paper presents a system which is used for the classification of biosensor signals. The proposed system is applied to the the synthesized and experimental data. The developed system showed good prediction perfomance.
Romas Baronas +3 more
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COPD phenotype description using principal components analysis
Respiratory Research, 2009 Background Airway inflammation in COPD can be measured using biomarkers such as induced sputum and FeNO. This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal ...
Vestbo Jørgen +5 more
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