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Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses [PDF]
Brain SciencesUnderstanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems.
Korey P. Wylie +3 more
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randPedPCA: rapid approximation of principal components from large pedigrees [PDF]
Genetics Selection EvolutionBackground Pedigrees continue to be extremely important in agriculture and conservation genetics, with the pedigrees of modern breeding programmes easily comprising millions of records.
Hanbin Lee +3 more
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Principal Components of Neural Convolution Filters
IEEE Access, 2022 Convolutions in neural networks are still essential on various vision tasks. To develop neural convolutions, this study focuses on Structured Receptive Field (SRF), representing a convolution filter as a linear combination of widely acting designed ...
Shota Fukuzaki, Masaaki Ikehara
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Principal components analysis of population admixture. [PDF]
PLoS ONE, 2012 With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology.
Jianzhong Ma, Christopher I Amos
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Principal component analysis in the spectral analysis of the dynamic laser speckle patterns [PDF]
Journal of the European Optical Society-Rapid Publications, 2014 Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle ...
Ribeiro K. M. +4 more
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Dynamic Functional Principal Components for Testing Causality
Signals, 2021 In this paper, we investigate the causality in the sense of Granger for functional time series. The concept of causality for functional time series is defined, and a statistical procedure of testing the hypothesis of non-causality is proposed.
Matthieu Saumard, Bilal Hadjadji
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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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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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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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