5 citations · 11 across the 7 of their papers we have counts for
7 papers
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…
Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
Zeqin Lin, Guangming Pan, Zhixiang Zhang +1
Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the h…
Asymptotic limits of spiked eigenvalues and eigenvectors of signal-plus-noise matrices with weak signals and heteroskedastic noise
Xiaoyu Liu, Yiming Liu, Guangming Pan +2
This paper is to study a signal-plus-noise model in high dimensional settings when the dimension and the sample size are comparable. Specifically, we assume that the noise has a ge…
Statistical inference in two-stage observation models including algorithmic randomness
Zhixiang Zhang, Sokbae Lee, Edgar Dobriban
Randomized algorithms, such as random sampling, random projections, and stochastic optimization, are increasingly used to reduce the computational cost of modern statistical analys…
Tracy-Widom law for the extreme eigenvalues of large signal-plus-noise matrices
Zhixiang Zhang, Guangming Pan
Let $\bY =\bR+\bX$ be an matrix, where $\bR$ is a rectangular diagonal matrix and $\bX$ consists of entries. This is a signal-plus-noise type model. Its signal…
Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices
Zhixiang Zhang, Shurong Zheng, Guangming Pan +1
We consider general high-dimensional spiked sample covariance models and show that their leading sample spiked eigenvalues and their linear spectral statistics are asymptotically i…
Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Edgar Dobriban, Weijie J. Su, Yachong Yang +1
Drawing statistical inferences from large datasets in a model-robust way is an important problem in statistics and data science. In this paper, we propose methods that are robust t…