principal components
Direct Estimation of Genetic Principal Components
... overall estimate of the covariance function. The point of departure for this article is the simple How do the results depend on the number of mea- observation that covariance matrices and covariance surements taken on ...
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Principal Components Analysis a method Useful in Identification of Clusters of Variables
... of components extracted during principal components analysis was equal to the number of variables that were put into ...18 components. The sum of variance accounted by these components ...
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Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation
... Principal components analysis (PCA) is a well-known statis- tical technique that has been widely applied to solve impor- tant signal processing problems like feature extraction, sig- nal estimation, ...
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Linkage analysis using principal components of gene expression data
... Principal-components (PC) analysis is a data reduction technique in which each component is a linear combina- tion of the phenotypes, each PC describing as much vari- ability of the phenotypes as possible ...
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Principal Components Analysis with Spline Optimal Transformations for Continuous Data
... nonlinear principal components and the original variables can be ...nonlinear principal components’ space and reconstruction the original ...nonlinear principal components and ...
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The ‘not-so-strange’ body in the mirror: A principal components analysis of direct and mirror self-observation : A principal components analysis of direct and mirror self-observation
... above. Principal Components Analyses (PCA) with varimax rotation was then used to investigate the structure underlying direct and mirror self-observation of the body, using scores from the ...resulting ...
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Correctness results for on-line robust principal components analysis
... Another interpretation of the original problem is as a robust principal components analysis (PCA) problem. Given a matrix of data, the goal of PCA is to compute a small number or orthogonal directions along ...
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COPD phenotype description using principal components analysis
... [16]. Principal components analysis (PCA) is the commonest form of factor analysis and reduces a large number of variables to a much smaller number of components, explaining the variability within ...
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Genetic mapping of principal components of canine pelvic morphology
... through principal component analysis (PCA), which classifies phenotypic variation into independent systems of correlated traits ...every principal component (PC). Principal components of ...
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Identification of gene gene interaction using principal components
... After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction effects is ...
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Linkage and association analyses of principal components in expression data
... The Genetic Analysis Workshop 15 data set consists of 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees. In ...
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Health Systems in Latin America: Principal Components of Attention
... and principal components to explore how health expenditures, poverty and inequalities are associated with healthcare indicators such as life expectancy, immunizations, treatments of- fered by health systems ...
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Application of Principal Components Analysis Results in Visual Network Analysis
... of principal components analysis application in economic and financial tasks demonstrates its efficiency in playing the role of a preprocessor in the process of network visual analysis (graphs, social ...
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Permutation validated principal components analysis of microarray data
... the principal components loadings for each of the ...first components of the PCA and components with high F-values are ...these components reflect the between-group ...compute ...
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Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components
... kernel principal components provide more parsimonious representation for the target regression func- tion, which is also reflected by the faster uniform convergence rates presented in Theorem 1, see also ...
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Amendments of a Stochastic Restricted Principal Components Regression Estimator in the Linear Model
... Abstract: Principal component Analysis (PCA) is one of the popular methods used to solve the multicollinearity ...restricted principal components (SRPC) regression ...the principal ...
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Analysis of Principal Components of the Sea Ice Concentration Fields in the Barents Sea
... the principal components of the sea ice concentration fields in the Barents Sea make it possible to distinguish three modes of inter- annual variability of the sea ice ...
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Spectrum sensing using low complexity principal components for cognitive radios
... components for detection; in Fig. 1, ‘pc’ represents the number of principal components included in test statistic. It is also clear that the first two PCs retain the most use- ful information as ...
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Taxonomical analysis of regional development by outranking relations on multiple principal components
... An illustrative application: We illustrate the use of the proposed method in classifying the districts of Bihar according to their levels of overall development. We have seventeen districts and five indicators of ...
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Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression
... the principal components were gen- erated on the residuals of the data that were corrected for the primary variable of ...These principal components contain residual “noise” within the data ...
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