5 papers
Bias-Corrected Multiplier Bootstrap Inference for Spectral Edges of Large Covariance Matrices
Xiucai Ding, Yichen Hu, Jiahui Xie
Inference for spectral edges of large covariance matrices is a fundamental problem in high-dimensional statistics. A major difficulty is that the largest non-spiked sample eigenval…
Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise
Xiucai Ding, Chao Shen, Hau-Tieng Wu
Multiview datasets are common in scientific and engineering applications, yet existing fusion methods offer limited theoretical guarantees, particularly in the presence of heteroge…
A Lanczos-Based Algorithmic Approach for Spike Detection in Large Sample Covariance Matrices
Charbel Abi Younes, Xiucai Ding, Thomas Trogdon
We introduce a new approach for estimating the number of spikes in a general class of spiked covariance models without directly computing the eigenvalues of the sample covariance m…
Structural Classification of Locally Stationary Time Series Based on Second-order Characteristics
Chen Qian, Xiucai Ding, Lexin Li
Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a numerically efficient, practically competitive, and theore…
Simultaneous Sieve Estimation and Inference for Time-Varying Nonlinear Time Series Regression
Xiucai Ding, Zhou Zhou
In this paper, we investigate time-varying nonlinear time series regression for a broad class of locally stationary time series. First, we propose sieve nonparametric estimators fo…