Debashis Paul 个人学术档案

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	总计	2019 年至今
引用	4101	1946
h 指数	22	18
i10 指数	28	23

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Jie PengUniversity of California, Davis在 ucdavis.edu 的电子邮件经过验证
Eric BairStatistical Scientist, Sciome在 sciome.com 的电子邮件经过验证
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Trevor HastieProfessor of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Jiming JiangProfessor of Statistics, University of California在 wald.ucdavis.edu 的电子邮件经过验证
Hongyu ZhaoYale University在 yale.edu 的电子邮件经过验证
Pei WangProfessor of genetics, Icahn School of Medicine at Mount Sinai在 mssm.edu 的电子邮件经过验证
Ravi Narasimhan在 stanfordalumni.org 的电子邮件经过验证
Tomoko MatsuoUniversity of Colorado, Boulder在 colorado.edu 的电子邮件经过验证
Minjie FanSoftware Engineer/Researcher, Google在 google.com 的电子邮件经过验证
Owen CarmichaelPennington Biomedical Research Center在 pbrc.edu 的电子邮件经过验证
Anandamayee MajumdarSan Francisco State University在 sfsu.edu 的电子邮件经过验证
Cong LiTakeda Pharmaceuticals Inc在 takeda.com 的电子邮件经过验证
Can YangHong Kong University of Science and Technology在 ust.hk 的电子邮件经过验证
Boaz NadlerWeizmann Institute of Science, Israel在 weizmann.ac.il 的电子邮件经过验证
Raymond K. W. WongTexas A&M University在 tamu.edu 的电子邮件经过验证
R PrenticeFred Hutchinson Cancer Research Center在 whi.org 的电子邮件经过验证
Karen A. McDonaldProfessor Emerita of Chemical Engineering, University of California at Davis在 ucdavis.edu 的电子邮件经过验证
Jack W SilversteinNorth Carolina State University在 math.ncsu.edu 的电子邮件经过验证
Douglas NychkaProfessor, Applied Mathematics and Statistics, Colorado School of Mines在 mines.edu 的电子邮件经过验证

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Debashis Paul

University of California, Davis and Indian Statistical Instititute, Kolkata

在 ucdavis.edu 的电子邮件经过验证

random matrix theory neuroimaging high-dimensional statistics


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Prediction by supervised principal components E Bair, T Hastie, D Paul, R Tibshirani Journal of the American Statistical Association 101 (473), 119-137, 2006	1025	2006
Asymptotics of sample eigenstructure for a large dimensional spiked covariance model D Paul Statistica Sinica, 1617-1642, 2007	886	2007
On the distribution of SINR for the MMSE MIMO receiver and performance analysis P Li, D Paul, R Narasimhan, J Cioffi IEEE Transactions on Information Theory 52 (1), 271-286, 2005	331	2005
Random matrix theory in statistics: A review D Paul, A Aue Journal of Statistical Planning and Inference 150, 1-29, 2014	216	2014
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	189	2013
PCA in high dimensions: An orientation IM Johnstone, D Paul Proceedings of the IEEE 106 (8), 1277-1292, 2018	151	2018
“Preconditioning” for feature selection and regression in high-dimensional problems D Paul, E Bair, T Hastie, R Tibshirani	149	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	146	2009
A Regularized Hotelling’s T² 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	128	2011
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	100	2009
Augmented sparse principal component analysis for high dimensional data D Paul, IM Johnstone arXiv preprint arXiv:1202.1242, 2012	89	2012
On high-dimensional misspecified mixed model analysis in genome-wide association study J Jiang, C Li, D Paul, C Yang, H Zhao	83*	2016
On the Marčenko–Pastur law for linear time series H Liu, A Aue, D Paul	78	2015
Consistency of restricted maximum likelihood estimators of principal components D Paul, J Peng	57	2009
AN ADAPTABLE GENERALIZATION OF HOTELLING’ST ² TEST IN HIGH DIMENSION H Li, A Aue, D Paul, J Peng, P Wang The Annals of Statistics 48 (3), 1815-1847, 2020	39*	2020
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	38	2014
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	38*	2011
Modeling tangential vector fields on a sphere M Fan, D Paul, TCM Lee, T Matsuo Journal of the American Statistical Association 113 (524), 1625-1636, 2018	29	2018
Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach D Paul, J Peng	27	2011
Spectral analysis of sample autocovariance matrices of a class of linear time series in moderately high dimensions L Wang, A Aue, D Paul	26*	2017

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