Ph.D. Thesis

• Debashis Paul (2005) - Nonparametric estimation of principal components. Ph.D. Thesis, Stanford University, Department of Statistics

Journal Papers

• Arvind Prasadan, Raj Rao Nadakuditi and Debashis Paul (2020) - Sparse equisigned PCA: algorithms and performance bounds in the noisy rank-1 setting. To appear in Electronic Journal of Statistics, Vol. 14, No. 1, 345-385. [arXiv1905.09369]


• Haoran Li, Alexander Aue and Debashis Paul (2020+) - High-dimensional general linear hypothesis tests via non-linear spectral shrinkage. To appear in Bernoulli. [arXiv1810.02043]


• Haoran Li, Alexander Aue, Debashis Paul, Jie Peng and Pei Wang (2020+) - An adaptable generalization of Hotelling's T² test in high dimension. To appear in The Annals of Statistics. [arXiv1609.08725]


• Iain M. Johnstone and Debashis Paul (2018) - PCA in high dimensions: an orientation. Proceedings of the IEEE. Vol. 106, No. 8, 1277-1292.


• Jung Won Hyun, Prabir Burman and Debashis Paul (2018) - Local linear estimation for spatial random processes with stochastic trend and stationary noise. Sankhya B, Vol. 80, No. 2, 369-394.


• Hao Yan, Owen Carmichael, Debashis Paul and Jie Peng (2018) - Estimating fiber orientation distribution from diffusion MRI with spherical needlets. Medical Image Analysis, Vol. 46, 57-72. [arXiv1612.07439]


• Minjie Fan, Debashis Paul, Thomas C. M. Lee and Tomoko Matsuo (2018) - A multi-resolution model for non-Gaussian random fields on a sphere with application to ionospheric electrostatic potentials. The Annals of Applied Statistics, Vol. 12, No. 1, 459-489. [arXiv1710.00153]


• Minjie Fan, Debashis Paul, Thomas C. M. Lee and Tomoko Matsuo (2018) - Modeling tangential vector fields on a sphere. Journal of the American Statistical Association, Vol. 113, No. 524, 1625-1636. [arXiv1612.08062]


• Siyuan Zhou, Debashis Paul and Jie Peng (2018) - Modeling subject-specific nonautonomous dynamics. Statistica Sinica, Vol. 28, No. 1, 423-447.


• Prabir Burman and Debashis Paul (2017) - Smooth predictive model fitting in regression. Journal of Multivariate Analysis, Vol. 155, 165-179. [online abstract]


• Lili Wang, Alexander Aue and Debashis Paul (2017) - Spectral analysis of sample autocovariance matrices of a class of linear time series in moderately high dimensions. Bernoulli, Vol. 23, No. 4A, 2181-2209. [arXiv1504.06360]


• Debashis Paul, Jie Peng and Prabir Burman (2016) - Nonparametric estimation of dynamics of monotone trajectories. The Annals of Statistics, Vol. 44, No. 6, 2401-2432. [arXiv1408.5339]


• Raymond K. W. Wong, Thomas C. M. Lee, Debashis Paul and Jie Peng (2016) - Fiber direction estimation, smoothing and tracking in diffusion MRI. The Annals of Applied Statistics, Vol. 10, No. 3, 1137-1156. [arXiv1406.0581]


• Jiming Jiang, Cong Li, Debashis Paul, Can Yang and Hongyu Zhao (2016) - High-dimensional genome-wide association study and misspecified mixed model analysis. The Annals of Statistics, Vol. 44, No. 5, 2127-2160. [arXiv1404.2355]


• Anandamayee Majumdar and Debashis Paul (2016) - Zero expectile processes and Bayesian spatial regression. Journal of Computational and Graphical Statistics, Vol. 25, No. 3, 727-747.


• Patrick Danaher, Debashis Paul and Pei Wang (2015) - Covariance-based analyses of biological pathways. Biometrika, Vol. 102, No. 3, 533-544.


• Haoyang Liu, Alexander Aue and Debashis Paul (2015) - On the Marcenko-Pastur law for linear time series. The Annals of Statistics, Vol. 43, No. 2, 675-712. [arXiv1310.7270]


• Jie Peng, Debashis Paul and Hans-Georg Muller (2014) - Time-warped growth processes, with applications to the modeling of boom-bust cycles in housing prices. The Annals of Applied Statistics, Vol. 8, No. 3, 1561-1582. [arXiv1411.5497]


• Iain M. Johnstone and Debashis Paul (2014) - Adaptation in some linear inverse problems. Stat, Vol. 3, 187-199. [arXiv1310.7149]


• Lili Wang and Debashis Paul (2014) - Limiting spectral distribution of renormalized separable sample covariance matrices when $p/n\to 0$. Journal of Multivariate Analysis, Vol. 126, 25-52. [arXiv1308.1766]


• Debashis Paul and Alexander Aue (2014) - Random matrix theory in statistics: a review. Journal of Statistical Planning and Inference, Vol. 150, 1-29.


• Owen Carmichael, Jun Chen, Debashis Paul and Jie Peng (2013) - Diffusion tensor smoothing through weighted Karcher means. Electronic Journal of Statistics, Vol. 7, 1913-1956. [arXiv1011.6298]


• Aharon Birnbaum, Iain M. Johnstone, Boaz Nadler and Debashis Paul (2013) - Minimax bounds for sparse PCA with noisy high-dimensional data. The Annals of Statistics, Vol. 41, 1055-1084. [arXiv1203.0967]


• Ethan Anderes and Debashis Paul (2012) - Shrinking the quadratic estimator of weak lensing. Physical Review, D, Vol. 85, 103003. [arXiv1110.1694]


• Jung Won Hyun, Prabir Burman and Debashis Paul (2012) - A regression approach for estimating the parameters of the covariance function of a stationary spatial random process. Journal of Statistical Planning and Inference, Vol. 142, 2330-2344.


• Lin Chen, Debashis Paul, Ross Prentice and Pei Wang (2011) - A regularized Hotelling's T² test for pathway analysis in proteomic studies. Journal of the American Statistical Association, Vol. 106, 1345-1360.


• Debashis Paul and Jie Peng (2011) - Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach. Electronic Journal of Statistics, Vol. 5, 1960-2003. [arXiv0807.1106]


• Debashis Paul, Jie Peng and Prabir Burman (2011) - Semiparametric modeling of autonomous nonlinear dynamical systems with applications to plant growth. The Annals of Applied Statistics, Vol. 5, 2078-2108. [arXiv0906.3501]


• Tomoko Matsuo, Doug Nychka and Debashis Paul (2011) - Nonstationary covariance modeling for incomplete data : Monte-carlo EM approach Computational Statistics and Data Analysis, Vol. 55, 2059-2073.


• Anandamayee Majumdar, Debashis Paul and Jason Kaye (2010) - Sensitivity analysis and model selection for a generalized convolution model for spatial processes. Bayesian Analysis, Vol. 5, 493-518.


• Anandamayee Majumdar, Debashis Paul and Dianne Bautista (2010) - A generalized convolution model for multivariate nonstationary spatial processes. Statistica Sinica, Vol. 20, 675-695 . [Supplementary material]


• Jie Peng and Debashis Paul (2009) - A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data . Journal of Computational and Graphical Statistics, Vol. 18, 995-1015. [arXiv0710.5343]


• Peter Hall, Young K. Lee, Byeong U. Park and Debashis Paul (2009) - Tie-respecting bootstrap methods for estimating distributions of sets and functions of eigenvalues . Bernoulli, Vol. 15, No. 2, 380-401. [arXiv0906.2128]


• Debashis Paul and Jie Peng (2009) - Consistency of restricted maximum likelihood estimators of principal components. [Technical report]. The Annals of Statistics, Vol. 37, 1229-1271.


• Debashis Paul and Jack W. Silverstein (2009) - No eigenvalues outside the support of limiting empirical spectral distribution of a separable covariance matrix. Journal of Multivariate Analysis, Vol. 100, Issue 1, 37-57. [Technical Report]


• Debashis Paul, Eric Bair, Trevor Hastie and Rob Tibshirani (2008) - Pre-conditioning for feature selection and regression in high-dimensional problems. The Annals of Statistics , Vol. 36, No. 4, 1595-1618. [Technical Report] [arXiv:math/0703858]


• Debashis Paul (2007) - Asymptotics of sample eigenstruture for a large dimensional spiked covariance model. Statistica Sinica, Vol. 17, No. 4, 1617-1642. [Technical Report (June, 2005 version)]


• Eric Bair, Trevor Hastie, Debashis Paul and Robert Tibshirani (2006) - Prediction by supervised principal components, Journal of the American Statistical Association, Vol. 101, No. 473, 119-137. [Technical Report (September, 2004 version)]


• Ping Li, Debashis Paul, Ravi Narasimhan and John Cioffi (2006) - On the distribution of SINR for the MMSE MIMO receiver and performance analysis. IEEE Transactions on Information Theory, Vol. 52, No. 1, 271-286. [Technical Report]

Invited Discussions

• Debashis Paul (2004) - Discussion on ``Statistical approaches to inverse problems''. Journal of the Royal Statistical Society, Series B, Vol. 66, 634-635.


• Debashis Paul and Lili Wang (2016) - Discussion of ``Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation''. Electronic Journal of Statistics, Vol. 10, 74-80.


• Debashis Paul and Anandamayee Majumdar (2016) - Discussion on ``Of quantiles and expectiles : consistent scoring functions, Choquet representations and forecastings'' by Ehm, Gneiting, Jordan and Kruger. To appear in Journal of Royal Statistical Society, Series B, Vol. 78.

Book Chapters

• Debashis Paul (2011) - Asymptotic distribution of the smallest eigenvalue of Wishart(N,n) when N,n --> $\infty$ such that N/n --> 0. In Nonparametric Statistical Methods and Related Topics : A Festschrift in Honor of Professor P K Bhattacharya on the Occasion of His 80th Birthday. (Eds.) Jiang, J., Roussas, G. G. and Samaniego, F. J. World Scientific.

Conference Proceedings

• Viswanath, V., Fletcher, E., Singh, B., Smith, N., Paul, D., Peng, J., Chen, J. and Carmichael, O. T. (2012) - Impact of DTI smoothing on the study of brain aging. Proceedings of the 34th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), Vol. 34, 94-97.

Technical Reports

• Debashis Paul and Iain Johnstone (2007) - Sparse principal component analysis for high dimensional data. [Technical report] [arXiv1202.1242]


• Debasis Sengupta and Debashis Paul (2004) - Some tests for logconcavity of life distributions .