1 citations · 1 across the 9 of their papers we have counts for
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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…
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…
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…
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…
Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
Jinyuan Chang, Cheng Yong Tang, Yuanzheng Zhu
In this study, we introduce a novel methodological framework called Bayesian Penalized Empirical Likelihood (BPEL), designed to address the computational challenges inherent in emp…