198 citations · 888 across the 47 of their papers we have counts for
33 papers · 1 filter
Bayesian Inference for Multivariate Monotone Densities
Kang Wang, Subhashis Ghosal
We consider a nonparametric Bayesian approach to estimation and testing for a multivariate monotone density. Instead of following the conventional Bayesian route of putting a prior…
Bayesian Inference for -Monotone Densities with Applications to Multiple Testing
Kang Wang, Subhashis Ghosal
Shape restriction, like monotonicity or convexity, imposed on a function of interest, such as a regression or density function, allows for its estimation without smoothness assumpt…
Posterior Contraction and Testing for Multivariate Isotonic Regression
Kang Wang, Subhashis Ghosal
We consider the nonparametric regression problem with multiple predictors and an additive error, where the regression function is assumed to be coordinatewise nondecreasing. We pro…
Coverage of Credible Intervals in Bayesian Multivariate Isotonic Regression
Kang Wang, Subhashis Ghosal
We consider the nonparametric multivariate isotonic regression problem, where the regression function is assumed to be nondecreasing with respect to each predictor. Our goal is to…
Bayesian inference in high-dimensional models
Sayantan Banerjee, Ismaël Castillo, Subhashis Ghosal
Models with dimension more than the available sample size are now commonly used in various applications. A sensible inference is possible using a lower-dimensional structure. In re…
Convergence Rates for Bayesian Estimation and Testing in Monotone Regression
Moumita Chakraborty, Subhashis Ghosal
Shape restrictions such as monotonicity on functions often arise naturally in statistical modeling. We consider a Bayesian approach to the problem of estimation of a monotone regre…
Unified Bayesian theory of sparse linear regression with nuisance parameters
Seonghyun Jeong, Subhashis Ghosal
We study frequentist asymptotic properties of Bayesian procedures for high-dimensional Gaussian sparse regression when unknown nuisance parameters are involved. Nuisance parameters…
Bayesian nonparametric tests for multivariate locations
Indrabati Bhattacharya, Subhashis Ghosal
In this paper, we propose novel, fully Bayesian non-parametric tests for one-sample and two-sample multivariate location problems. We model the underlying distribution using a Diri…
Bayesian Inference on Multivariate Medians and Quantiles
Indrabati Bhattacharya, Subhashis Ghosal
In this paper, we consider Bayesian inference on a class of multivariate median and the multivariate quantile functionals of a joint distribution using a Dirichlet process prior. S…
Bayesian Linear Regression for Multivariate Responses Under Group Sparsity
Bo Ning, Seonghyun Jeong, Subhashis Ghosal
We study frequentist properties of a Bayesian high-dimensional multivariate linear regression model with correlated responses. The predictors are separated into many groups and the…
Posterior Contraction and Credible Sets for Filaments of Regression Functions
Wei Li, Subhashis Ghosal
A filament consists of local maximizers of a smooth function when moving in a certain direction. A filamentary structure is an important feature of the shape of an object and i…
Bayesian mode and maximum estimation and accelerated rates of contraction
William Weimin Yoo, Subhashis Ghosal
We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines…
Bayesian Detection of Image Boundaries
Meng Li, Subhashis Ghosal
Detecting boundary of an image based on noisy observations is a fundamental problem of image processing and image segmentation. For a -dimensional image (), th…
Bayesian inference for higher order ordinary differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Often the regression function appearing in fields like economics, engineering, biomedical sciences obeys a system of higher order ordinary differential equations (ODEs). The equati…
Bayesian two-step estimation in differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Ordinary differential equations (ODEs) are used to model dynamic systems appearing in engineering, physics, biomedical sciences and many other fields. These equations contain unkno…
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
William Weimin Yoo, Subhashis Ghosal
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and…
Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Often the regression function is specified by a system of ordinary differential equations (ODEs) involving some unknown parameters. Typically analytical solution of the ODEs is not…
Bayesian estimation in differential equation models
Prithwish Bhaumik, Subhashis Ghosal
Ordinary differential equations (ODEs) are used to model dynamic systems appearing in engineering, physics, biomedical sciences and many other fields. These equations contain unkno…
Adaptive Bayesian density regression for high-dimensional data
Weining Shen, Subhashis Ghosal
Density regression provides a flexible strategy for modeling the distribution of a response variable given predictors by letting that the conditio…
Adaptive Bayesian procedures using random series priors
Weining Shen, Subhashis Ghosal
We consider a prior for nonparametric Bayesian estimation which uses finite random series with a random number of terms. The prior is constructed through distributions on the numbe…
Bayesian estimation of a sparse precision matrix
Sayantan Banerjee, Subhashis Ghosal
We consider the problem of estimating a sparse precision matrix of a multivariate Gaussian distribution, including the case where the dimension is large. Gaussian graphical mod…
Optimal two-stage procedures for estimating location and size of the maximum of a multivariate regression function
Eduard Belitser, Subhashis Ghosal, Harry van Zanten
We propose a two-stage procedure for estimating the location $\boldsμ$ and size M of the maximum of a smooth d-variate regression function f. In the first stage, a preliminary esti…
Posterior convergence rates for estimating large precision matrices using graphical models
Sayantan Banerjee, Subhashis Ghosal
We consider Bayesian estimation of a precision matrix, when can be much larger than the available sample size . It is well known that consistent estimation in su…
MCMC-free adaptive Bayesian procedures using random series prior
Weining Shen, Subhashis Ghosal
We consider priors for several nonparametric Bayesian models which use finite random series with a random number of terms. The prior is constructed through distributions on the num…