Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency...

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Vydáno v:	Journal of the Royal Statistical Society. Series B, Statistical methodology Ročník 72; číslo 1; s. 3 - 25
Hlavní autoři:	Chun, Hyonho, Keleş, Sündüz
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford, UK Oxford, UK : Blackwell Publishing Ltd 2010 Blackwell Publishing Ltd Wiley-Blackwell Blackwell Royal Statistical Society Oxford University Press
Edice:	Journal of the Royal Statistical Society Series B
Témata:	Attention Biostatistics Chromatin immuno-precipitation Consistent estimators Data Dimension reduction Dimensionality reduction Direction vectors Estimators Exact sciences and technology Experiments Gene expression General topics Genomics Lasso Least squares Least squares method Linear inference, regression Linear regression Mathematical vectors Mathematics Matrices Microarrays Multivariate analysis Original Paradigms Partial least squares Probability and statistics Regression analysis Sample size Sciences and techniques of general use Simulation Sparsity Statistical methods Statistics Studies Threshing Variable and feature selection Chromatin immuno-precipitation Microarrays Lasso Multivariate regression Regression analysis Microarray Gene expression Asymptotic convergence Implementation Statistical method Statistical regression Dimension reduction Linear combination Simulation Sparsity Least squares method Variable and feature selection Partial least squares Variable selection
ISSN:	1369-7412, 1467-9868
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