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Prediction by Supervised Principal Components
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique calledExpand
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ASYMPTOTICS OF SAMPLE EIGENSTRUCTURE FOR A LARGE DIMENSIONAL SPIKED COVARIANCE MODEL
This paper deals with a multivariate Gaussian observation model where the eigenvalues of the covariance matrix are all one, except for a finite number which are larger. Of interest is the asymptoticExpand
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On the distribution of SINR for the MMSE MIMO receiver and performance analysis
This correspondence studies the statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for the minimum mean-square error (MMSE) receiver in multiple-input multiple-outputExpand
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MINIMAX BOUNDS FOR SPARSE PCA WITH NOISY HIGH-DIMENSIONAL DATA.
We study the problem of estimating the leading eigenvectors of a high-dimensional population covariance matrix based on independent Gaussian observations. We establish a lower bound on the minimaxExpand
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"Preconditioning" for feature selection and regression in high-dimensional problems
We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function,Expand
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A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data
In this article, we consider the problem of estimating the eigenvalues and eigenfunctions of the covariance kernel (i.e., the functional principal components) from sparse and irregularly observedExpand
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Augmented sparse principal component analysis for high dimensional data
We study the problem of estimating the leading eigenvectors of a high-dimensional population covariance matrix based on independent Gaussian observations. We establish lower bounds on the rates ofExpand
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No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix
We consider a class of matrices of the form C"n=(1/N)A"n^1^/^2X"nB"nX"n^*xA"n^1^/^2, where X"n is an nxN matrix consisting of i.i.d. standardized complex entries, A"n^1^/^2 is a nonnegative definiteExpand
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A Regularized Hotelling’s T2 Test for Pathway Analysis in Proteomic Studies
Recent proteomic studies have identified proteins related to specific phenotypes. In addition to marginal association analysis for individual proteins, analyzing pathways (functionally related setsExpand
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Random matrix theory in statistics: A review
Abstract We give an overview of random matrix theory (RMT) with the objective of highlighting the results and concepts that have a growing impact in the formulation and inference of statisticalExpand
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