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Mean-Reverting 4/2 Principal Components Model. Financial Applications
Risks, 2021 In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance.
Marcos Escobar-Anel, Zhenxian Gong
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A genealogical interpretation of principal components analysis. [PDF]
PLoS Genetics, 2009 Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to
Gil McVean
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Principal components analysis for mixtures with varying concentrations
Modern Stochastics: Theory and Applications, 2021 Principal Component Analysis (PCA) is a classical technique of dimension reduction for multivariate data. When the data are a mixture of subjects from different subpopulations one can be interested in PCA of some (or each) subpopulation separately.
Olena Sugakova, Rostyslav Maiboroda
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Power swing detecting method using principal components analysis
Energy Reports, 2021 During power swing, the distance protection is easily affected by the oscillations of voltage and current which may lead to the mal-operation of the protection. Therefore, a power swing detecting unit is needed to cooperate with distance protection.
Hao Wang +7 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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Principal Components of CMB non-Gaussianity [PDF]
, 2015 The skew-spectrum statistic introduced by Munshi & Heavens (2010) has recently been used in studies of non-Gaussianity from diverse cosmological data sets including the detection of primary and secondary non-Gaussianity of Cosmic Microwave Background ...
Munshi, Dipak, Regan, Donough
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Integrating Data Transformation in Principal Components Analysis [PDF]
, 2015 Principal component analysis (PCA) is a popular dimension-reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets.
Hu, Jianhua +2 more
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Investigating dark energy experiments with principal components [PDF]
, 2009 We use a principal component approach to contrast different kinds of probes of dark energy, and to emphasize how an array of probes can work together to constrain an arbitrary equation of state history w(z). We pay particular attention to the role of the
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