D. Paul

发表

ASYMPTOTICS OF SAMPLE EIGENSTRUCTURE FOR A LARGE DIMENSIONAL SPIKED COVARIANCE MODEL

D. Paul, 2007 .

Random matrix theory in statistics: A review

D. Paul, A. Aue, Alexander Aue, 2014 .

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

B. Nadler, I. Johnstone, D. Paul, 2012, Annals of statistics.

Prediction by Supervised Principal Components

R. Tibshirani, T. Hastie, D. Paul, 2006 .

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

R. Tibshirani, T. Hastie, D. Paul, 2007, math/0703858.

Augmented sparse principal component analysis for high dimensional data

I. Johnstone, D. Paul, 2012, 1202.1242.

Nonparametric estimation of principal components

D. Paul, 2005 .

Covariance-based analyses of biological pathways.

D. Paul, P. Danaher, P. Wang, 2015, Biometrika.

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

Debashis Paul, Pei Wang, Lin S. Chen, 2011, Journal of the American Statistical Association.

Semiparametric modeling of autonomous nonlinear dynamical systems with application to plant growth

Jie Peng, Debashis Paul, Prabir Burman, 2011, 1111.7089.

A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data

Jie Peng, Debashis Paul, D. Paul, 2007, 0710.5343.

On the Marčenko-Pastur law for linear time series

D. Paul, A. Aue, Haoyang Liu, 2013, 1310.7270.

Spectral analysis of sample autocovariance matrices of a class of linear time series in moderately high dimensions

D. Paul, A. Aue, Lili Wang, 2017 .

Title Random matrix theory in statistics : A review Permalink

D. Paul, A. Aue, Alexander Aue, 2014 .

High-dimensional general linear hypothesis tests via non-linear spectral shrinkage

Debashis Paul, Alexander Aue, D. Paul, 2018, Bernoulli.

Adaptation in some linear inverse problems

Debashis Paul, Iain M. Johnstone, I. Johnstone, 2014 .

Zero Expectile Processes and Bayesian Spatial Regression

Debashis Paul, Anandamayee Majumdar, D. Paul, 2016 .

A regression approach for estimating the parameters of the covariance function of a stationary spatial random process

Debashis Paul, Prabir Burman, Jung Won Hyun, 2012 .

A GENERALIZED CONVOLUTION MODEL FOR MULTIVARIATE NONSTATIONARY SPATIAL PROCESSES

Debashis Paul, Anandamayee Majumdar, D. Paul, 2010 .

Shrinking the quadratic estimator of weak lensing

D. Paul, E. Anderes, 2011, 1110.1694.

Geometric kernel smoothing of tensor fields

Owen Carmichael, D. Paul, Jie Peng, 2010, 1011.6298.

Spectral analysis of linear time series in moderately high dimensions

D. Paul, A. Aue, Lili Wang, 2015, 1504.06360.

High-Dimensional Linear Models: A Random Matrix Perspective

Debashis Paul, Jamshid Namdari, Lili Wang, 2020, Sankhya A.

Techno-economic process modelling and Monte Carlo simulation data of uncertainty quantification in field-grown plant-based manufacturing

D. Paul, S. Nandi, K. McDonald, 2021, Data in brief.

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

R. Tibshirani, T. Hastie, D. Paul, 2007, math/0703858.

Sensitivity analysis and model selection for a generalized convolution model for spatial processes

D. Paul, Anandamayee Majumdar, J. Kaye, 2010 .

FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI.

Jie Peng, Debashis Paul, Raymond K. W. Wong, 2014, The annals of applied statistics.

Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach

Jie Peng, Debashis Paul, D. Paul, 2008, 0807.1106.

Local Linear Estimation for Spatial Random Processes with Stochastic Trend and Stationary Noise

D. Paul, P. Burman, J. Hyun, 2018, Sankhya B.

Smooth predictive model fitting in regression

Debashis Paul, Prabir Burman, D. Paul, 2017, J. Multivar. Anal..

Variance Estimation and Confidence Intervals from Genome-wide Association Studies Through High-dimensional Misspecified Mixed Model Analysis.

D. Paul, Jiming Jiang, Hongyu Zhao, 2022, Journal of statistical planning and inference.

Variance Estimation and Confidence Intervals from High-dimensional Genome-wide Association Studies Through Misspecified Mixed Model Analysis

Debashis Paul, Hongyu Zhao, Cecilia Dao, 2021, 2101.06638.

Adaptation in a class of linear inverse problems

I. Johnstone, D. Paul, 2013, 1310.7149.

On high-dimensional misspecified mixed model analysis in genome-wide association study

Can Yang, Debashis Paul, Hongyu Zhao, 2016 .

High-dimensional genome-wide association study and misspecified mixed model analysis

Can Yang, Debashis Paul, Hongyu Zhao, 2014, 1404.2355.

FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI.

Raymond K. W. Wong, Thomas C.M. Lee, D. Paul, 2014, The annals of applied statistics.

Fiber Direction Estimation in Diffusion MRI

Jie Peng, Debashis Paul, Thomas C. M. Lee, 2014 .

PCA in High Dimensions: An Orientation

Debashis Paul, Iain M. Johnstone, I. Johnstone, 2018, Proceedings of the IEEE.

Nonparametric estimation of dynamics of monotone trajectories

D. Paul, Jie Peng, P. Burman, 2014, 1408.5339.

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

John M. Cioffi, Ravi Narasimhan, Ping Li, 2006, IEEE Transactions on Information Theory.

Modeling Tangential Vector Fields on a Sphere

Debashis Paul, Tomoko Matsuo, Thomas C.M. Lee, 2016, Journal of the American Statistical Association.

Nonstationary covariance modeling for incomplete data: Monte Carlo EM approach

Douglas W. Nychka, Debashis Paul, Tomoko Matsuo, 2011, Comput. Stat. Data Anal..

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

Debashis Paul, Jack W. Silverstein, J. W. Silverstein, 2009, J. Multivar. Anal..

Multiresolution Modeling of High‐Latitude Ionospheric Electric Field Variability and Impact on Joule Heating Using SuperDARN Data

Thomas C.M. Lee, D. Paul, J. Ruohoniemi, 2021, Journal of geophysical research. Space physics.

Impact of DTI smoothing on the study of brain aging

Jun Chen, Evan Fletcher, Baljeet Singh, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Improved Shrinkage Prediction under a Spiked Covariance Structure

D. Paul, Gourab Mukherjee, Trambak Banerjee, 2021, J. Mach. Learn. Res..

Inference on the dynamics of COVID-19 in the United States

D. Paul, S. Bhattacharjee, S. Chaudhuri, 2022, Scientific Reports.

Inference on the dynamics of the COVID pandemic from observational data

D. Paul, S. Bhattacharjee, S. Chaudhuri, 2021, medRxiv.

Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices

Jie Peng, Debashis Paul, Hans-Georg Muller, 2014, 1411.5497.

Analysis of Variability of Functionals of Recombinant Protein Production Trajectories Based on Limited Data

D. Paul, S. Nandi, K. McDonald, 2021, bioRxiv.

Modeling subject-specific nonautonomous dynamics.

D. Paul, Jie Peng, Siyuan Zhou, 2018, Statistica Sinica.

Semiparametric modeling of autonomous nonlinear dynamical systems with applications

Jie Peng, Debashis Paul, Prabir Burman, 2009, 0906.3501.

Diffusion tensor smoothing through weighted Karcher means.

Jie Peng, Debashis Paul, Jun Chen, 2013, Electronic journal of statistics.

An adaptable generalization of Hotelling’s $T^{2}$ test in high dimension

Jie Peng, Debashis Paul, Pei Wang, 2016, The Annals of Statistics.

Estimating fiber orientation distribution from diffusion MRI with spherical needlets

Hao Yan, Owen T. Carmichael, Jie Peng, 2016, Medical Image Anal..

CONSISTENCY OF RESTRICTED MAXIMUM LIKELIHOOD ESTIMATORS OF PRINCIPAL COMPONENTS

Debashis Paul, D. Paul, Jie Peng, 2008, 0805.0465.

Tie-respecting bootstrap methods for estimating distributions of sets and functions of eigenvalues

Debashis Paul, Peter Hall, Byeong U. Park, 2009, 0906.2128.

Estimating Fiber Orientation Distribution through Blockwise Adaptive Thresholding with Application to HCP Young Adults Data

Debashis Paul, Seungyong Hwang, Thomas C.M. Lee, 2020 .

2 9 N ov 2 01 0 Geometric kernel smoothing of tensor fields

Owen Carmichael, D. Paul, Jie Peng, 2010 .

Limiting spectral distribution of renormalized separable sample covariance matrices when p/ni → 0

Debashis Paul, Lili Wang, D. Paul, 2013, J. Multivar. Anal..

An adaptable generalization of Hotelling’s $T^{2}$ test in high dimension

D. Paul, Jie Peng, Pei Wang, 2016, The Annals of Statistics.