4 citations · 9 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…