- Debashis Paul (2005) - Nonparametric estimation of principal
components. Ph.D. Thesis, Stanford University, Department of
Jung Won Hyun, Prabir Burman and Debashis Paul (2018+) - Local linear
estimation for spatial random processes with stochastic trend and stationary
noise. To appear in Sankhya, Series B.
Hao Yan, Owen Carmichael, Debashis Paul and Jie Peng (2018+) -
Estimating fiber orientation distribution from diffusion MRI with spherical needlets.
To appear in Medical Image Analysis.
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.
To appear in The Annals of Applied Statistics.
Minjie Fan, Debashis Paul, Thomas C.M. Lee and Tomoko Matsuo (2018+) -
Modeling tangential vector fields on a sphere. To appear in Journal
of the American Statistical Association.
- Siyuan Zhou, Debashis Paul and Jie Peng (2018) - Modeling subject-specific
nonautonomous dynamics. Statistica Sinica, Vol. 28, 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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,
- 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 .
- 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.
- 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) -
of restricted maximum likelihood estimators of principal components.
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.
- 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.
- Debashis Paul (2007) - Asymptotics of sample eigenstruture for a large dimensional
spiked covariance model. Statistica Sinica, Vol. 17, No. 4, 1617-1642.
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.
- Debashis Paul (2004) - Discussion on ``Statistical approaches to inverse
problems''. Journal of the Royal Statistical Society, Series B, Vol. 66,
- 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.
- 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.