72 citations · 765 across the 35 of their papers we have counts for
39 papers
An adaptive -type test for high-dimensional white noise
Jinyuan Chang, Jing He, Weiming Li +1
We propose a new -type test for white noise which allows the dimension of the time series to either (i) be a fixed constant, or (ii) diverge with the sample size . The…
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…
Empirical likelihood approach for high-dimensional moment restrictions with dependent data
Jinyuan Chang, Qiao Hu, Zhentao Shi +1
Economic and financial models -- such as vector autoregressions, local projections, and multivariate volatility models -- feature complex dynamic interactions and spillovers across…
HDTSA: An R package for high-dimensional time series analysis
Jinyuan Chang, Jing He, Chen Lin +1
High-dimensional time series analysis has become increasingly important in fields such as finance, economics, and biology. The two primary tasks for high-dimensional time series an…
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…
Identification and estimation for matrix time series CP-factor models
Jinyuan Chang, Yue Du, Guanglin Huang +1
We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method of Chang et al. (2023) for whi…
On the modelling and prediction of high-dimensional functional time series
Jinyuan Chang, Qin Fang, Xinghao Qiao +1
We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series is large in relation to the length…
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…
Statistical inferences for complex dependence of multimodal imaging data
Jinyuan Chang, Jing He, Jian Kang +1
Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this…
Efficiently handling constraints with Metropolis-adjusted Langevin algorithm
Jinyuan Chang, Cheng Yong Tang, Yuanzheng Zhu
In this study, we investigate the performance of the Metropolis-adjusted Langevin algorithm in a setting with constraints on the support of the target distribution. We provide a ri…
Statistical inference for high-dimensional spectral density matrix
Jinyuan Chang, Qing Jiang, Tucker S. McElroy +1
The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component process…
Testing the martingale difference hypothesis in high dimension
Jinyuan Chang, Qing Jiang, Xiaofeng Shao
In this paper, we consider testing the martingale difference hypothesis for high-dimensional time series. Our test is built on the sum of squares of the element-wise max-norm of th…
Modelling matrix time series via a tensor CP-decomposition
Jinyuan Chang, Jing He, Lin Yang +1
We consider to model matrix time series based on a tensor CP-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions,…
Edge differentially private estimation in the -model via jittering and method of moments
Jinyuan Chang, Qiao Hu, Eric D. Kolaczyk +2
A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off…
Culling the herd of moments with penalized empirical likelihood
Jinyuan Chang, Zhentao Shi, Jia Zhang
Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection. While a large pool of val…
Central limit theorems for high dimensional dependent data
Jinyuan Chang, Xiaohui Chen, Mingcong Wu
Motivated by statistical inference problems in high-dimensional time series data analysis, we first derive non-asymptotic error bounds for Gaussian approximations of sums of high-d…
An autocovariance-based learning framework for high-dimensional functional time series
Jinyuan Chang, Cheng Chen, Xinghao Qiao +1
Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables is comparable to, or…
Testing for unit roots based on sample autocovariances
Jinyuan Chang, Guanghui Cheng, Qiwei Yao
We propose a new unit-root test for a stationary null hypothesis against a unit-root alternative . Our approach is nonparametric as only assumes that the process c…
Optimal covariance matrix estimation for high-dimensional noise in high-frequency data
Jinyuan Chang, Qiao Hu, Cheng Liu +1
We consider high-dimensional measurement errors with high-frequency data. Our objective is on recovering the high-dimensional cross-sectional covariance matrix of the random errors…
High-dimensional empirical likelihood inference
Jinyuan Chang, Song Xi Chen, Cheng Yong Tang +1
High-dimensional statistical inference with general estimating equations are challenging and remain less explored. In this paper, we study two problems in the area: confidence set…