Subhashis Ghosal Mulgrave, J. J., & Ghosal, S. (2020). Bayesian Inference in Nonparanormal Graphical Models. BAYESIAN ANALYSIS, 15(2), 449–475. https://doi.org/10.1214/19-BA1159 Wei, R., & Ghosal, S. (2020). Contraction properties of shrinkage priors in logistic regression. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 207, 215–229. https://doi.org/10.1016/j.jspi.2019.12.004 Belitser, E., & Ghosal, S. (2020). EMPIRICAL BAYES ORACLE UNCERTAINTY QUANTIFICATION FOR REGRESSION. ANNALS OF STATISTICS, 48(6), 3113–3137. https://doi.org/10.1214/19-AOS1845 Ghosal, S. (2020, March 3). Preface of the Special Issue in Honor of Professor Jayanta Kumar Ghosh. SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY. https://doi.org/10.1007/s13171-020-00199-z Roy, A., Ghosal, S., Prescott, J., & Choudhury, K. R. (2019). BAYESIAN MODELING OF THE STRUCTURAL CONNECTOME FOR STUDYING ALZHEIMER'S DISEASE. ANNALS OF APPLIED STATISTICS, 13(3), 1791–1816. https://doi.org/10.1214/19-AOAS1257 Ning, B., Ghosal, S., & Thomas, J. (2019). Bayesian Method for Causal Inference in Spatially-Correlated Multivariate Time Series. BAYESIAN ANALYSIS, 14(1), 1–28. https://doi.org/10.1214/18-BA1102 Zhu, R., & Ghosal, S. (2019). Bayesian Semiparametric ROC surface estimation under verification bias. Computational Statistics & Data Analysis, 133, 40–52. https://doi.org/10.1016/j.csda.2018.09.003 Yoo, W. W., & Ghosal, S. (2019). Bayesian mode and maximum estimation and accelerated rates of contraction. BERNOULLI, 25(3), 2330–2358. https://doi.org/10.3150/18-BEJ1056 Zhu, R., & Ghosal, S. (2019). Bayesian nonparametric estimation of ROC surface under verification bias. STATISTICS IN MEDICINE, 38(18), 3361–3377. https://doi.org/10.1002/sim.8181 Du, X., & Ghosal, S. (2019). Multivariate Gaussian network structure learning. Journal of Statistical Planning and Inference, 199, 327–342. https://doi.org/10.1016/j.jspi.2018.07.009 Du, X., & Ghosal, S. (2018). Bayesian Discriminant Analysis Using a High Dimensional Predictor. Sankhya A, 80(S1), 112–145. https://doi.org/10.1007/s13171-018-0140-z Li, X., & Ghosal, S. (2018). Bayesian classification of multiclass functional data. ELECTRONIC JOURNAL OF STATISTICS, 12(2), 4669–4696. https://doi.org/10.1214/18-EJS1522 Das, P., & Ghosal, S. (2017). Analyzing ozone concentration by Bayesian spatio-temporal quantile regression. Environmetrics, 28(4). Li, M., & Ghosal, S. (2017). BAYESIAN DETECTION OF IMAGE BOUNDARIES. ANNALS OF STATISTICS, 45(5), 2190–2217. https://doi.org/10.1214/16-aos1523 Suarez, A. J., & Ghosal, S. (2017). Bayesian Estimation of Principal Components for Functional Data. BAYESIAN ANALYSIS, 12(2), 311–333. https://doi.org/10.1214/16-ba1003 Bhaumik, P., & Ghosal, S. (2017). Bayesian inference for higher-order ordinary differential equation models. Journal of Multivariate Analysis, 157, 103–114. Das, P., & Ghosal, S. (2017). Bayesian quantile regression using random B-spline series prior. Computational Statistics & Data Analysis, 109, 121–143. Bhaumik, P., & Ghosal, S. (2017). Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models. Bernoulli, 23(4B), 3537–3570. Shen, W. N., & Ghosal, S. (2017). Posterior contraction rates of density derivative estimation. Sankhya-Series A-Mathematical Statistics and Probability, 79(2), 336–354. Shen, W. N., & Ghosal, S. (2016). Adaptive Bayesian density regression for high-dimensional data. Bernoulli, 22(1), 396–420. Suarez, A. J., & Ghosal, S. (2016). Bayesian Clustering of Functional Data Using Local Features. BAYESIAN ANALYSIS, 11(1), 71–98. https://doi.org/10.1214/14-ba925 Ghosal, S. (2016). Editorial overview: Special issue on Bayesian nonparametrics. Subhashis Ghosal Guest editor of the special issue on Bayesian nonparametrics. Electronic Journal of Statistics, 10(2), 3217–3218. Luo, S., & Ghosal, S. (2016). Forward selection and estimation in high dimensional single index models. Statistical Methodology, 33, 172–179. https://doi.org/10.1016/j.stamet.2016.09.002 Ghosal, S., Turnbull, B., Zhang, H. H., & Hwang, W. Y. (2016). Sparse penalized forward selection for support vector classification. Journal of Computational and Graphical Statistics, 25(2), 493–514. Yoo, W. W., & Ghosal, S. (2016). Supremum norm posterior contraction and credible sets for nonparametric multivariate regression. Annals of Statistics, 44(3), 1069–1102. Shen, W. N., & Ghosal, S. (2015). Adaptive Bayesian procedures using random series priors. Scandinavian Journal of Statistics: Theory and Applications, 42(4), 1194–1213. Banerjee, S., & Ghosal, S. (2015). Bayesian structure learning in graphical models. Journal of Multivariate Analysis, 136, 147–162. Ghosal, S. (2015). Discussion of "Frequentist coverage of adaptive nonparametric Bayesian credible sets". Annals of Statistics, 43(4), 1455–1462. Li, M., & Ghosal, S. (2015). Fast translation invariant multiscale image denoising. IEEE Transactions on Image Processing, 24(12), 4876–4887. Luo, S., & Ghosal, S. (2015). Prediction consistency of forward iterated regression and selection technique. Statistics & Probability Letters, 107, 79–83. https://doi.org/10.1016/j.spl.2015.08.005 Ghoshal, S., Kleijn, B., Vaart, A., van, & Zanten, H. (2015). Special issue on Bayesian nonparametrics. Journal of Statistical Planning and Inference, 166, 1–1. Li, M., & Ghosal, S. (2014). Bayesian Multiscale Smoothing of Gaussian Noised Images. BAYESIAN ANALYSIS, 9(3), 733–758. https://doi.org/10.1214/14-ba871 Gu, J. Z., Ghosal, S., & Kleiner, D. E. (2014). Bayesian ROC curve estimation under verification bias. Statistics in Medicine, 33(29), 5081–5096. McKay Curtis, S., Banerjee, S., & Ghosal, S. (2014). Fast Bayesian model assessment for nonparametric additive regression. Computational Statistics & Data Analysis, 71, 347–358. https://doi.org/10.1016/j.csda.2013.05.012 Banerjee, S., & Ghosal, S. (2014). Posterior convergence rates for estimating large precision matrices using graphical models. Electronic Journal of Statistics, 8, 2111–2137. Shen, W. N., Tokdar, S. T., & Ghosal, S. (2013). Adaptive Bayesian multivariate density estimation with Dirichlet mixtures. Biometrika, 100(3), 623–640. White, J. T., & Ghosal, S. (2013). Denoising three-dimensional and colored images using a Bayesian multi-scale model for photon counts. Signal Processing, 93(11), 2906–2914. Belitser, E., Ghosal, S., & Zanten, H. (2012). Optimal two-stage procedures for estimating location and size of the maximum of a multivariate regression function. Annals of Statistics, 40(6), 2850–2876. White, J. T., & Ghosal, S. (2011). Bayesian smoothing of photon-limited images with applications in astronomy. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 73, 579–599. Ghosal, S., & Roy, A. (2011). Identifiability of the proportion of null hypotheses in skew-mixture models for the p-value distribution. Electronic Journal of Statistics, 5, 329–341. Ghosal, S., & Roy, A. (2011). Predicting false discovery proportion under dependence. Journal of the American Statistical Association, 106(495), 1208–1218. Clarke, B., & Ghosal, S. (2010). Reference priors for exponential families with increasing dimension. Electronic Journal of Statistics, 4, 737–780. Wu, Y. F., & Ghosal, S. (2010). The L-1-consistency of Dirichlet mixtures in multivariate Bayesian density estimation. Journal of Multivariate Analysis, 101(10), 2411–2419. Gu, J. Z., & Ghosal, S. (2009). Bayesian ROC curve estimation under binormality using a rank likelihood. Journal of Statistical Planning and Inference, 139(6), 2076–2083. Ghosal, S., & Roy, A. (2009). Bayesian nonparametric approach to multiple testing. Perspectives in mathematical sciences i: probability and statistics, 7, 139–164. Roy, A., Ghosal, S., & Rosenberger, W. F. (2009). Convergence properties of sequential Bayesian D-optimal designs. Journal of Statistical Planning and Inference, 139(2), 425–440. https://doi.org/10.1016/j.jspi.2008.04.025 Hwang, W. Y., Zhang, H. H., & Ghosal, S. (2009). FIRST: Combining forward iterative selection and shrinkage in high dimensional sparse linear regression. Statistics and Its Interface, 2(3), 341–348. https://doi.org/10.4310/sii.2009.v2.n3.a7 Gu, J., Ghosal, S., & Roy, A. (2008). Bayesian bootstrap estimation of ROC curve. Statistics in Medicine, 27(26), 5407–5420. https://doi.org/10.1002/sim.3366 Gu, J., & Ghosal, S. (2008). Strong approximations for resample quantile processes and application to ROC methodology. Journal of Nonparametric Statistics, 20(3), 229–240. https://doi.org/10.1080/10485250801954128 Tang, Y., & Ghosal, S. (2007). A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities. Computational Statistics & Data Analysis, 51(9), 4424–4437. https://doi.org/10.1016/j.csda.2006.06.020 Ghosal, S., & Van Der Vaart, A. (2007). Convergence rates of posterior distributions for noniid observations. ANNALS OF STATISTICS, 35(1), 192–223. https://doi.org/10.1214/009053606000001172 Tang, Y., Ghosal, S., & Roy, A. (2007). Nonparametric Bayesian estimation of positive false discovery rates. BIOMETRICS, 63(4), 1126–1134. https://doi.org/10.1111/j.1541-0420.2007.00819.x Tang, Y., & Ghosal, S. (2007). Posterior consistency of Dirichlet mixtures for estimating a transition density. Journal of Statistical Planning and Inference, 137(6), 1711–1726. https://doi.org/10.1016/j.jspi.2006.03.007 Ghosal, S., & Vaart, A. (2007). Posterior convergence rates of dirichlet mixtures at smooth densities. ANNALS OF STATISTICS, 35(2), 697–723. https://doi.org/10.1214/009053606000001271 Ghosal, S., & Roy, A. (2006). Posterior consistency of Gaussian process prior for nonparametric binary regression. ANNALS OF STATISTICS, 34(5), 2413–2429. https://doi.org/10.1214/009053606000000795 Choudhuri, N., Ghosal, S., & Roy, A. (2004). Bayesian estimation of the spectral density of a time series. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 99(468), 1050–1059. https://doi.org/10.1198/016214504000000557 Choudhuri, N., Ghosal, S., & Roy, A. (2004). Contiguity of the Whittle measure for a Gaussian time series. BIOMETRIKA, 91(1), 211–218. https://doi.org/10.1093/biomet/91.1.211 Belitser, E., & Ghosal, S. (2003). Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution. Annals of Statistics, 31(2), 536–559. Ghosal, S., Lember, J., & Vaart, A. (2003). On Bayesian adaptation. ACTA APPLICANDAE MATHEMATICAE, 79(1-2), 165–175. https://doi.org/10.1023/A:1025856016236 Amewou-Atisso, M., Ghosal, S., Ghosh, J. K., & Ramamoorthi, R. V. (2003). Posterior consistency for semi-parametric regression problems. BERNOULLI, 9(2), 291–312. https://doi.org/10.3150/bj/1068128979 Ghosal, S. (1999). Asymptotic normality of posterior distributions in high-dimensional linear models. BERNOULLI, 5(2), 315–331. https://doi.org/10.2307/3318438