7 papers
Cauchy-Aggregated Ridge Tests for High-Dimensional Factor Pricing Models
Ping Zhao, Dan Zhuang
In high-dimensional factor pricing models, the power of regularized alpha tests depends on the ridge parameter, whose optimal value varies with the unknown direction of pricing err…
Spectral Dynamics of DeepWalk Embeddings for Dynamic Network Change-Point Detection
Houlin Zhou, Yejin Wang, Xufei Tang +1
Dynamic networks describe evolving relational systems in which abrupt structural changes may signal anomalous events or important transitions. Detecting such changes requires disti…
Change-Point Detection for Heterogeneous High-Dimensional Functional Time Series
Xufei Tang, Dan Zhuang, Houlin Zhou
High-dimensional functional panels consist of temporally ordered curves observed across many subjects and naturally exhibit heterogeneous structural changes. Under sparse subject-l…
Sparse -spatial-median clustering for high-dimensional data
Ping Zhao, Dan Zhuang, Long Feng
We propose a robust clustering framework for high-dimensional data with heavy tails and a large fraction of irrelevant variables. The method replaces the mean updates of Lloyd's $K…
Adaptive Test for High Dimensional Quantile Regression
Ping Zhao, Zhenyu Liu, Dan Zhuang
Testing high-dimensional quantile regression coefficients is crucial, as tail quantiles often reveal more than the mean in many practical applications. Nevertheless, the sparsity p…
Randomised Euler-Maruyama Method for SDEs with Hölder Continuous Drift Coefficient Driven by -stable Lévy Process
Jianhai Bao, Haitao Wang, Yue Wu +1
In this paper, we examine the performance of randomised Euler-Maruyama (EM) method for additive time-inhomogeneous SDEs with an irregular drift driven by symmetric -table proces…
Spatial Sign based Direct Sparse Linear Discriminant Analysis for High Dimensional Data
Dan Zhuang, Long Feng
This paper investigates the robust linear discriminant analysis (LDA) problem with elliptical distributions in high-dimensional data. We propose a robust classification method, nam…