5 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity
Shiyun Xu, Zhiqi Bu, Pratik Chaudhari +1
Interpretable machine learning has demonstrated impressive performance while preserving explainability. In particular, neural additive models (NAM) offer the interpretability to th…
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…
The Asymptotic Distribution of Modularity in Weighted Signed Networks
Rong Ma, Ian Barnett
Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network's edge weight or adjacenc…
Novel Non-Negative Variance Estimator for (Modified) Within-Cluster Resampling
Daniel Xu, Pamela Shaw, Ian Barnett
This article proposes a novel variance estimator for within-cluster resampling (WCR) and modified within-cluster resampling (MWCR) - two existing methods for analyzing longitudinal…