A ridge penalized principal-components approach based on heritability for high-dimensional data.
about
Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies.Using item response theory to model multiple phenotypes and their joint heritability in family data.Global Individual Ancestry Using Principal Components for Family DataPrincipal component of explained variance: An efficient and optimal data dimension reduction framework for association studies.Clustering and principal-components approach based on heritability for mapping multiple gene expressions.Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies.Refining multivariate disease phenotypes for high chip heritabilityAn Effective Method to Identify Heritable Components from Multivariate PhenotypesAn Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.Testing for familial aggregation of functional traits.Variable selection in canonical discriminant analysis for family studies.
P2860
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P2860
A ridge penalized principal-components approach based on heritability for high-dimensional data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2007年の論文
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2007年学术文章
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2007年学术文章
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2007年学术文章
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2007年学术文章
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2007年学术文章
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2007年學術文章
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name
A ridge penalized principal-co ...... ity for high-dimensional data.
@ast
A ridge penalized principal-co ...... ity for high-dimensional data.
@en
type
label
A ridge penalized principal-co ...... ity for high-dimensional data.
@ast
A ridge penalized principal-co ...... ity for high-dimensional data.
@en
prefLabel
A ridge penalized principal-co ...... ity for high-dimensional data.
@ast
A ridge penalized principal-co ...... ity for high-dimensional data.
@en
P356
P1433
P1476
A ridge penalized principal-co ...... ity for high-dimensional data.
@en
P2093
P304
P356
10.1159/000102991
P50
P577
2007-05-25T00:00:00Z