3 papers
math.ST2026
Limiting eigen-structure of spiked sample covariance matrices under missing observations
Haotian Cheng, Huiqin Li, Yanqing Yin +1
High-dimensional Principal Component Analysis (PCA) has become an essential tool in modern data analysis, offering dimensionality reduction and feature extraction. However, the pre…
math.ST2026
The Geometry of Spectral Fluctuations: On Near-Optimal Conditions for Universal Gaussian CLTs, with Statistical Applications
Yanqing Yin, Wang Zhou
We study linear spectral statistics of high dimensional sample covariance matrices in a regime where the empirical spectral distribution remains governed by the classical sample co…
math.ST2026
Inference for Spiked Eigenstructure under Generalized Covariance and Correlation Models
Yanqing Yin, Wang Zhou
In high-dimensional principal component analysis, important inferential targets include both leading spikes and the associated principal eigenspaces. Such problems arise naturally…