World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
35
Citations
7133
World Ranking
2726
National Ranking
1116

Engineering and Technology

D-Index
31
Citations
5989
World Ranking
9646
National Ranking
2746

Overview

Subhashis Ghosal is affiliated with North Carolina State University in the United States. Their research primarily spans the field of Mathematics, with a concentrated focus on Statistics and Probability.

The scientist's work includes significant contributions to the following subfields:

  • Statistics and Probability
  • Artificial Intelligence
  • Genetics
  • Molecular Biology
  • Statistics, Probability and Uncertainty

Ghosal's research topics encompass a range of statistical and probabilistic methodologies, notably:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Advanced Statistical Methods and Models
  • Bayesian Methods and Mixture Models
  • Genetic and phenotypic traits in livestock
  • Spectroscopy and Chemometric Analyses
  • Probabilistic and Robust Engineering Design

The scientist has published extensively in various academic venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • Electronic Journal of Statistics
  • Bernoulli
  • The Annals of Statistics
  • Journal of Statistical Planning and Inference

Among recent papers, notable works are:

  • "Bayesian linear regression for multivariate responses under group sparsity," 2020, Bernoulli
  • "Empirical Bayes oracle uncertainty quantification for regression," 2020, The Annals of Statistics
  • "Contraction properties of shrinkage priors in logistic regression," 2020, Journal of Statistical Planning and Inference
  • "Posterior contraction in sparse generalized linear models," 2020, Biometrika
  • "Unified Bayesian theory of sparse linear regression with nuisance parameters," 2021, Electronic Journal of Statistics

Frequent collaborators in their research include:

  • Seonghyun Jeong
  • Moumita Chakraborty
  • Kang Wang
  • Eduard Belitser

Best Publications

  • Convergence rates of posterior distributions

    Subhashis Ghosal;Jayanta K. Ghosh;Aad W. van der Vaart

  • Fundamentals of Nonparametric Bayesian Inference

    Subhashis Ghosal;Aad van der Vaart

  • POSTERIOR CONSISTENCY OF DIRICHLET MIXTURES IN DENSITY ESTIMATION

    S. Ghosal;J. K. Ghosh;R. V. Ramamoorthi

  • Convergence rates of posterior distributions for non-i.i.d. observations

    Subhashis Ghosal;Aad van der Vaart

  • Rates of convergence for Bayes and maximum likelihood estimation for mixture of normal densities

    Subhashis Ghosal;Aad W. van der Vaart

  • Posterior convergence rates of Dirichlet mixtures at smooth densities

    Subhashis Ghosal;Aad van der Vaart

  • Posterior consistency of Gaussian process prior for nonparametric binary regression

    Subhashis Ghosal;Anindya Roy

  • Convergence rates of posterior distributions for noniid observations

    Subhashis Ghosal;Aad van der Vaart

  • Convergence rates for density estimation with Bernstein polynomials

    Subhashis Ghosal

  • Adaptive Bayesian multivariate density estimation with Dirichlet mixtures

    Weining Shen;Surya T. Tokdar;Subhashis Ghosal

  • Bayesian Estimation of the Spectral Density of a Time Series

    Nidhan Choudhuri;Subhashis Ghosal;Anindya Roy

  • Extensions of the strong law of large numbers of Marcinkiewicz and Zygmund for dependent variables

    T. K. Chandra;S. Ghosal

  • Kullback Leibler property of kernel mixture priors in Bayesian density estimation

    Yuefeng Wu;Subhashis Ghosal

  • Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution

    Eduard Belitser;Subhashis Ghosal

  • Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

    William Weimin Yoo;Subhashis Ghosal

  • Bayesian structure learning in graphical models

    Sayantan Banerjee;Subhashis Ghosal

  • On convergence of posterior distributions

    Subhashis Ghosal;Jayanta K. Ghosh;Tapas Samanta

  • Nonparametric Bayesian model selection and averaging

    Subhashis Ghosal;Jüri Lember;Aad van der Vaart

  • Testing monotonicity of a regression function.

    S. Ghosal;A.W. van der Vaart

  • Asymptotic normality of posterior distributions in high-dimensional linear models

    Subhashis Ghosal

  • Bayesian Nonparametrics: The Dirichlet process, related priors and posterior asymptotics

    Unknown

  • Nonparametric Bayesian model selection and averaging

    S. Ghosal;J. Lember;A.W. van der Vaart

Frequent Co-Authors

Aad van der Vaart
Aad van der Vaart Delft University of Technology
Jayanta K. Ghosh
Jayanta K. Ghosh Purdue University West Lafayette
Hao Helen Zhang
Hao Helen Zhang University of Arizona
David E. Kleiner
David E. Kleiner National Institutes of Health
William F. Rosenberger
William F. Rosenberger George Mason University

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