1 citations · 1 across the 4 of their papers we have counts for
5 papers
Nonparametric Identification of Latent Dimension under Heavy-Tailed and Mixed-Signals
Chetkar Jha
Principal component analysis and factor analysis are foundational to the study of high-dimensional data, yet their efficacy depends entirely on correctly identifying the latent dim…
Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Chetkar Jha, Mingyao Li, Ian Barnett
Clustering of single-cell RNA sequencing (scRNA-seq) datasets can give key insights into the biological functions of cells. Therefore, it is not surprising that network-based commu…
Confidence Intervals for the Number of Components in Factor Analysis and Principal Components Analysis via Subsampling
Chetkar Jha, Ian Barnett
Factor analysis (FA) and principal component analysis (PCA) are popular statistical methods for summarizing and explaining the variability in multivariate datasets. By default, FA…
Multiple Hypothesis Testing To Estimate The Number of Communities in Sparse Stochastic Block Models
Chetkar Jha, Mingyao Li, Ian Barnett
Network-based clustering methods frequently require the number of communities to be specified \emph{a priori}. Moreover, most of the existing methods for estimating the number of c…
A Nonparametric Bayesian Method for Clustering of High-Dimensional Mixed Dataset
Chetkar Jha
The paper is motivated from clustering problem in high-throughput mixed datasets. Clustering of such datasets can provide much insight into biological associations. An open problem…