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- Publications
- Influence

Prediction by Supervised Principal Components

- E. Bair, T. Hastie, D. Paul, R. Tibshirani
- Mathematics
- 1 March 2006

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 called… Expand

ASYMPTOTICS OF SAMPLE EIGENSTRUCTURE FOR A LARGE DIMENSIONAL SPIKED COVARIANCE MODEL

- D. Paul
- Mathematics
- 2007

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 asymptotic… Expand

- 508
- 75
- Open Access

On the distribution of SINR for the MMSE MIMO receiver and performance analysis

- P. Li, D. Paul, R. Narasimhan, J. Cioffi
- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 2006

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-output… Expand

MINIMAX BOUNDS FOR SPARSE PCA WITH NOISY HIGH-DIMENSIONAL DATA.

- Aharon Birnbaum, I. Johnstone, B. Nadler, D. Paul
- Medicine, Mathematics
- Annals of statistics
- 5 March 2012

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 minimax… Expand

"Preconditioning" for feature selection and regression in high-dimensional problems

- D. Paul, E. Bair, T. Hastie, R. Tibshirani
- Mathematics
- 28 March 2007

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

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 observed… Expand

Augmented sparse principal component analysis for high dimensional data

- D. Paul, I. Johnstone
- Mathematics
- 6 February 2012

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 of… Expand

No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix

- D. Paul, J. Silverstein
- Computer Science, Mathematics
- J. Multivar. Anal.
- 2009

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 definite… Expand

A Regularized Hotelling’s T2 Test for Pathway Analysis in Proteomic Studies

- L. Chen, D. Paul, R. Prentice, P. Wang
- Medicine, Mathematics
- Journal of the American Statistical Association
- 1 December 2011

Recent proteomic studies have identified proteins related to specific phenotypes. In addition to marginal association analysis for individual proteins, analyzing pathways (functionally related sets… Expand

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 statistical… Expand