25 citations · 72 across the 13 of their papers we have counts for
13 papers
Ridge partial correlation screening for ultrahigh-dimensional data
Run Wang, An Nguyen, Somak Dutta +1
Variable selection in ultrahigh-dimensional linear regression is challenging due to its high computational cost. Therefore, a screening step is usually conducted before variable se…
On the usefulness of lattice approximations for fractional Gaussian fields
Somak Dutta, Debashis Mondal
Fractional Gaussian fields provide a rich class of spatial models and have a long history of applications in multiple branches of science. However, estimation and inference for fra…
Exploratory Factor Analysis of Data on a Sphere
Fan Dai, Karin S. Dorman, Somak Dutta +1
Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure tha…
Bayesian iterative screening in ultra-high dimensional linear regressions
Run Wang, Somak Dutta, Vivekananda Roy
Variable selection in ultra-high dimensional linear regression is often preceded by a screening step to significantly reduce the dimension. Here we develop a Bayesian variable scre…
Fast matrix-free methods for model-based personalized synthetic MR imaging
Subrata Pal, Somak Dutta, Ranjan Maitra
Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statist…
Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings
Dongjin Li, Somak Dutta, Vivekananda Roy
We develop a Bayesian variable selection method, called SVEN, based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the mode…
A Matrix--free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Data
Fan Dai, Somak Dutta, Ranjan Maitra
This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer obser…
Adjusting for Spatial Effects in Genomic Prediction
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong +1
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect ph…
A novel sandwich algorithm for empirical Bayes analysis of rank data
Arnab Kumar Laha, Somak Dutta, Vivekananda Roy
Rank data arises frequently in marketing, finance, organizational behavior, and psychology. Most analysis of rank data reported in the literature assumes the presence of one or mor…
A note on marginal correlation based screening
Run Wang, Somak Dutta, Vivekananda Roy
Independence screening methods such as the two sample -test and the marginal correlation based ranking are among the most widely used techniques for variable selection in ultrah…
Supplement to "Markov Chain Monte Carlo Based on Deterministic Transformations"
Somak Dutta, Sourabh Bhattacharya
This is a supplement to the article "Markov Chain Monte Carlo Based on Deterministic Transformations" available at http://arxiv.org/abs/1106.5850
Markov Chain Monte Carlo Based on Deterministic Transformations
Somak Dutta, Sourabh Bhattacharya
In this article we propose a novel MCMC method based on deterministic transformations T: X x D --> X where X is the state-space and D is some set which may or may not be a subset o…
Multiplicative random walk Metropolis-Hastings on the real line
Somak Dutta
In this article we propose multiplication based random walk Metropolis Hastings (MH) algorithm on the real line. We call it the random dive MH (RDMH) algorithm. This algorithm, eve…