2 papers
stat.ME2025
Inference on the proportion of variance explained in principal component analysis
Ronan Perry, Snigdha Panigrahi, Jacob Bien +1
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low…
stat.ME2025
Thinning a Wishart Random Matrix
Ameer Dharamshi, Anna Neufeld, Lucy L. Gao +2
Recent work has explored data thinning, a generalization of sample splitting that involves decomposing a (possibly matrix-valued) random variable into independent components. In th…