115 citations · 172 across the 29 of their papers we have counts for
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Posterior Concentration for Gaussian Process Priors under Rescaled and Hierarchical Matérn and Confluent Hypergeometric Covariance Functions
Xiao Fang, Anindya Bhadra
In nonparameteric Bayesian approaches, Gaussian stochastic processes can serve as priors on real-valued function spaces. Existing literature on the posterior convergence rates unde…
Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual
Ksheera Sagar, Sayantan Banerjee, Jyotishka Datta +1
Precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is diff…
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions
Pulong Ma, Anindya Bhadra
The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Matérn class is that it is p…
Prediction risk for the horseshoe regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li +2
We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage i…
Global-Local Mixtures
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Global-local mixtures are derived from the Cauchy-Schlomilch and Liouville integral transformation identities. We characterize well-known normal-scale mixture distributions includi…
The Horseshoe+ Estimator of Ultra-Sparse Signals
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
We propose a new prior for ultra-sparse signal detection that we term the "horseshoe+ prior." The horseshoe+ prior is a natural extension of the horseshoe prior that has achieved s…