Pati, Debdeep individual record
Associate Professor
Positions:
selected publications
Academic Articles20
  • Sabnis, G., Pati, D., & Bhattacharya, A. (2019). Compressed Covariance Estimation with Automated Dimension Learning. Sankhya A. 81(2), 466-481.
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  • Geng, J., Bhattacharya, A., & Pati, D. (2019). Probabilistic Community Detection With Unknown Number of Communities. Journal of the American Statistical Association. 114(526), 1-32.
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  • Bhattacharya, A., Pati, D., & Yang, Y. (2019). Bayesian fractional posteriors. Annals of Statistics. 47(1), 39-66.
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  • Sarkar, A., Pati, D., Chakraborty, A., Mallick, B. K., & Carroll, R. J. (2018). Bayesian Semiparametric Multivariate Density Deconvolution. Journal of the American Statistical Association. 113(521), 0-0.
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  • Norets, A., & Pati, D. (2017). ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES. Econometric Theory. 33(4), 980-1012.
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  • Li, H., & Pati, D. (2017). Variable selection using shrinkage priors. Computational Statistics & Data Analysis. 107, 107-119.
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  • Bhattacharya, A., Dunson, D. B., Pati, D., & Pillai, N. S. (2016). Sub-optimality of some continuous shrinkage priors. Stochastic Processes and their Applications. 126(12), 3828-3842.
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  • Zhang, Z., Pati, D., & Srivastava, A. (2015). Bayesian clustering of shapes of curves. Journal of Statistical Planning and Inference. 166, 171-186.
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  • Tang, Y., Sinha, D., Pati, D., Lipsitz, S., & Lipshultz, S. (2015). Bayesian partial linear model for skewed longitudinal data. Biostatistics. 16(3), 441-453.
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  • Pati, D., & Bhattacharya, A. (2015). Adaptive Bayesian inference in the Gaussian sequence model using exponential-variance priors. Statistics & Probability Letters. 103, 100-104.
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  • Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2015). Dirichlet–Laplace Priors for Optimal Shrinkage. Journal of the American Statistical Association. 110(512), 1479-1490.
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  • Pati, D., Bhattacharya, A., & Cheng, G. (2015). Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior. Journal of Machine Learning Research. 16, 2837-2851.
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  • Gu, K., Pati, D., & Dunson, D. B. (2014). Bayesian Multiscale Modeling of Closed Curves in Point Clouds.. Journal of the American Statistical Association. 109(508), 1481-1494.
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  • Sarkar, A., Mallick, B. K., Staudenmayer, J., Pati, D., & Carroll, R. J. (2014). Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors. Journal of Computational and Graphical Statistics. 23(4), 1101-1125.
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  • Cervone, D., Pillai, N. S., Pati, D., Berbeco, R., & Lewis, J. H. (2014). A location-mixture autoregressive model for online forecasting of lung tumor motion. Annals of Applied Statistics. 8(3), 1341-1371.
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  • Pati, D., & Dunson, D. B. (2014). Bayesian nonparametric regression with varying residual density. Annals of the Institute of Statistical Mathematics. 66(1), 1-31.
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  • Bhattacharya, A., Pati, D., & Dunson, D. (2014). Anisotropic function estimation using multi-bandwidth Gaussian processes. Annals of Statistics. 42(1), 352-381.
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  • Pati, D., Bhattacharya, A., Pillai, N. S., & Dunson, D. (2014). Posterior contraction in sparse Bayesian factor models for massive covariance matrices. Annals of Statistics. 42(3), 1102-1130.
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  • Pati, D., Dunson, D. B., & Tokdar, S. T. (2013). Posterior consistency in conditional distribution estimation. Journal of Multivariate Analysis. 116, 456-472.
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  • Pati, D., Reich, B. J., & Dunson, D. B. (2011). Bayesian geostatistical modelling with informative sampling locations.. BIOMETRIKA. 98(1), 35-48.
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Chapters1
  • Zhang, Z., Pati, D., & Srivastava, A. (2015). Bayesian Shape Clustering. Nonparametric Bayesian Inference in Biostatistics. (pp. 57-75). Springer International Publishing.
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Conference Papers1