Debashis Paul
Cited by
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Prediction by supervised principal components
E Bair, T Hastie, D Paul, R Tibshirani
Journal of the American Statistical Association, 2006
Asymptotics of sample eigenstructure for a large dimensional spiked covariance model
D Paul
Statistica Sinica 17 (4), 1617-1642, 2007
On the distribution of SINR for the MMSE MIMO receiver and performance analysis
P Li, D Paul, R Narasimhan, J Cioffi
Information Theory, IEEE Transactions on 52 (1), 271-286, 2006
Minimax bounds for sparse PCA with noisy high-dimensional data
A Birnbaum, IM Johnstone, B Nadler, D Paul
Annals of statistics 41 (3), 1055, 2013
Random matrix theory in statistics: A review
D Paul, A Aue
Journal of Statistical Planning and Inference 150, 1-29, 2014
“Preconditioning” for feature selection and regression in high-dimensional problems
D Paul, E Bair, T Hastie, R Tibshirani
Annals of Statistics 36 (4), 1595-1618, 2008
A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data
J Peng, D Paul
Journal of Computational and Graphical Statistics 18 (4), 995-1015, 2009
Augmented sparse principal component analysis for high dimensional data
D Paul, IM Johnstone
Arxiv preprint arXiv:1202.1242, 2012
No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix
D Paul, JW Silverstein
Journal of Multivariate Analysis 100 (1), 37-57, 2009
A Regularized Hotelling’s T^2 Test for Pathway Analysis in Proteomic Studies
LS Chen, D Paul, RL Prentice, P Wang
Journal of the American Statistical Association 106 (496), 1345-1360, 2011
On the Marčenko–Pastur law for linear time series
H Liu, A Aue, D Paul
Annals of Statistics 43 (2), 675-712, 2015
On high-dimensional misspecified mixed model analysis in genome-wide association study
J Jiang, C Li, D Paul, C Yang, H Zhao
The Annals of Statistics 44 (5), 2127-2160, 2016
Pca in high dimensions: An orientation
IM Johnstone, D Paul
Proceedings of the IEEE 106 (8), 1277-1292, 2018
Consistency of restricted maximum likelihood estimators of principal components
D Paul, J Peng
The Annals of Statistics 37 (3), 1229-1271, 2009
Nonstationary covariance modeling for incomplete data: Monte Carlo EM approach
T Matsuo, DW Nychka, D Paul
Computational Statistics & Data Analysis 55 (6), 2059-2073, 2011
Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach
D Paul, J Peng
Electronic Journal of Statistics 5, 1960-2003, 2011
A generalized convolution model for multivariate nonstationary spatial processes
A Majumdar, D Paul, D Bautista
Statistica Sinica, 675-695, 2010
Nonparametric estimation of principal components.
D Paul
Diffusion tensor smoothing through weighted Karcher means
O Carmichael, J Chen, D Paul, J Peng
Electronic journal of statistics 7, 1913, 2013
Limiting spectral distribution of renormalized separable sample covariance matrices when p/n→ 0
L Wang, D Paul
Journal of Multivariate Analysis 126, 25-52, 2014
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Articles 1–20