2 citations · 2 across the 4 of their papers we have counts for
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Spectral Analysis of Gram Matrices with Missing at Random Observations: Convergence, Central Limit Theorems, and Applications in Statistical Inference
Huiqin Li, Guangming Pan, Yanqing Yin +1
Motivated by the statistical inference using the Gram matrix in the context of missing at random observations, this paper investigates the spectral properties of the random matrice…
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…
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…
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…
Limiting behavior of bilinear forms for the resolvent of sample covariance matrices under elliptical distribution with applications
Yanqing Yin, Wang Zhou
In this paper, we introduce a joint central limit theorem (CLT) for specific bilinear forms, encompassing the resolvent of the sample covariance matrix under an elliptical distribu…