15 citations · 21 across the 7 of their papers we have counts for
10 papers
Monitoring Adverse Events Through Bayesian Nonparametric Clustering Across Studies
Shijie Yuan, Kevin Roberts, Noirrit Kiran Chandra +2
We introduce a Bayesian nonparametric inference approach for aggregate adverse event (AE) monitoring across studies. The proposed model seamlessly integrates external data from his…
A Sparse Factor Model for Clustering High-Dimensional Longitudinal Data
Zihang Lu, Noirrit Kiran Chandra
Recent advances in engineering technologies have enabled the collection of a large number of longitudinal features. This wealth of information presents unique opportunities for res…
Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis
Noirrit Kiran Chandra, David B. Dunson, Jason Xu
Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of…
Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials
Noirrit Kiran Chandra, Abhra Sarkar, John F. de Groot +2
The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with…
Bayesian Scalable Precision Factor Analysis for Massive Sparse Gaussian Graphical Models
Noirrit Kiran Chandra, Peter Mueller, Abhra Sarkar
We propose a novel approach to estimating the precision matrix of multivariate Gaussian data that relies on decomposing them into a low-rank and a diagonal component. Such decompos…
Escaping the curse of dimensionality in Bayesian model based clustering
Noirrit Kiran Chandra, Antonio Canale, David B. Dunson
Bayesian mixture models are widely used for clustering of high-dimensional data with appropriate uncertainty quantification. However, as the dimension of the observations increases…
High-dimensional Asymptotic Theory of Bayesian Multiple Testing Procedures Under General Dependent Setup and Possible Misspecification
Noirrit Kiran Chandra, Sourabh Bhattacharya
In this article, we investigate the asymptotic properties of Bayesian multiple testing procedures under general dependent setup, when the sample size and the number of hypotheses b…
A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering
Noirrit Kiran Chandra, Richa Singh, Sourabh Bhattacharya
MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of gene expression. In recent years, there has been a tremendous and growing interest among researchers to…
Asymptotic Theory of Dependent Bayesian Multiple Testing Procedures Under Possible Model Misspecification
Noirrit K. Chandra, Sourabh Bhattacharya
We study asymptotic properties of Bayesian multiple testing procedures and provide sufficient conditions for strong consistency under general dependence structure. We also consider…
Non-marginal Decisions: A Novel Bayesian Multiple Testing Procedure
Noirrit K. Chandra, Sourabh Bhattacharya
In this paper we consider the problem of multiple testing when the hypotheses are dependent. In most of the existing literature, either Bayesian or non-Bayesian, the decision rules…