3 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
High-Dimensional Invariant Tests of Multivariate Normality Based on Radial Concentration
Xin Bing, Derek Latremouille
While the problem of testing multivariate normality has received considerable attention in the classical low-dimensional setting where the sample size is much larger than the f…
A New Regression Lens on Multi-Class Classification
Xin Bing, Bingqing Li, Marten Wegkamp
Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class set…
Inference in High-dimensional Multivariate Response Regression with Hidden Variables
Xin Bing, Wei Cheng, Huijie Feng +1
This paper studies the inference of the regression coefficient matrix under multivariate response linear regressions in the presence of hidden variables. A novel procedure for cons…
Detecting approximate replicate components of a high-dimensional random vector with latent structure
Xin Bing, Florentina Bunea, Marten Wegkamp
High-dimensional feature vectors are likely to contain sets of measurements that are approximate replicates of one another. In complex applications, or automated data collection, t…
Inference in latent factor regression with clusterable features
Xin Bing, Florentina Bunea, Marten Wegkamp
Regression models, in which the observed features and the response depend, jointly, on a lower dimensional, unobserved, latent vector , with $K<…