11 papers
Testing for functional white noise in high dimensions
Jinyuan Chang, Qing Jiang, Xinghao Qiao +1
White noise testing is a fundamental problem in time series analysis. Yet it remains largely unsolved for high-dimensional functional time series, despite the growing attention thi…
CP-factorization for high dimensional tensor time series and double projection iterations
Jinyuan Chang, Guanglin Huang, Qiwei Yao +1
We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decompos…
Autoregressive networks with dependent edges
Jinyuan Chang, Qin Fang, Eric D. Kolaczyk +2
We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity…
Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and -values
Jinyuan Chang, Chenlong Li, Cheng Yong Tang +1
We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with de…
Adapting to noise tails in private linear regression
Jinyuan Chang, Lin Yang, Mengyue Zha +1
While the traditional goal of statistics is to infer population parameters, modern practice increasingly demands protection of individual privacy. One way to address this need is t…
Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization
Jinyuan Chang, Yue Du, Jing He +1
We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. T…