118 citations · 689 across the 53 of their papers we have counts for
4 papers · 2 filters
Scalable Bayesian shrinkage and uncertainty quantification for high-dimensional regression
Bala Rajaratnam, Doug Sparks, Kshitij Khare +1
Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncert…
Bayesian inference for Gaussian graphical models beyond decomposable graphs
Kshitij Khare, Bala Rajaratnam, Abhishek Saha
Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is…
Extracting Common Time Trends from Concurrent Time Series: Maximum Autocorrelation Factors with Application to Tree Ring Time Series Data
Matz A. Haugen, Bala Rajaratnam, Paul Switzer
Concurrent time series commonly arise in various applications, including when monitoring the environment such as in air quality measurement networks, weather stations, oceanographi…
Towards a sparse, scalable, and stably positive definite (inverse) covariance estimator
Sang-Yun Oh, Bala Rajaratnam, Joong-Ho Won
High dimensional covariance estimation and graphical models is a contemporary topic in statistics and machine learning having widespread applications. An important line of research…