155 citations · 955 across the 56 of their papers we have counts for
4 papers · 1 filter
Bayesian shrinkage
Anirban Bhattacharya, Debdeep Pati, Natesh S. Pillai +1
Penalized regression methods, such as regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality properties under spars…
Causal inference from factorial designs using the potential outcomes model
Tirthankar Dasgupta, Natesh S. Pillai, Donald B. Rubin
A framework for causal inference from two-level factorial designs is proposed. The framework utilizes the concept of potential outcomes that lies at the center stage of causal infe…
Posterior contraction in sparse Bayesian factor models for massive covariance matrices
Debdeep Pati, Anirban Bhattacharya, Natesh S. Pillai +1
Sparse Bayesian factor models are routinely implemented for parsimonious dependence modeling and dimensionality reduction in high-dimensional applications. We provide theoretical u…
Statistical inference for dynamical systems: a review
Kevin McGoff, Sayan Mukherjee, Natesh S. Pillai
The topic of statistical inference for dynamical systems has been studied extensively across several fields. In this survey we focus on the problem of parameter estimation for non-…