Jinyuan Chang

CP-factorization for high-dimensional tensor time series and double projection iterations

Jinyuan Chang, Guanglin Huang, Qiwei Yao, Long Yu

Abstract 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 decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed …

cn, gb (code pays fourni par la source)

CP-factorization for high dimensional tensor time series and double projection iterations

Jinyuan Chang, Guanglin Huang, Qiwei Yao, Long Yu

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 decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based …

cn, gb (code pays fourni par la source)

Identification and estimation for matrix time-series CP-factor models

Jinyuan Chang, Yue Du, Guanglin Huang, Qiwei Yao

We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method (J. R. Stat. Soc. Ser. B. Stat. Methodol. 85 (2023) 127–148) for which the convergence rates of the associated estimators may …

cn, gb (code pays fourni par la source)

Autoregressive networks with dependent edges

Jinyuan Chang, Fang Qin, Eric D. Kolaczyk, Peter W. MacDonald et autres

Abstract We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity, and other stylized features often observed in real network data. By assuming the edges of networks at each time …

cn, au, ca, gb (code pays fourni par la source)

Adapting to Noise Tails in Private Linear Regression

Jinyuan Chang, Lin Yang, Mengyue Zha, Wen-Xin Zhou

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 to adapt classical statistical procedures into privacy-preserving algorithms. In this article, we develop differentially private tail-robust …

Adapting to Noise Tails in Private Linear Regression

Jinyuan Chang, Lin Yang, Mengyue Zha, Wen-Xin Zhou

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 to adapt classical statistical procedures into privacy-preserving algorithms. In this article, we develop differentially private tail-robust …

Testing Independence and Conditional Independence in High Dimensions via Coordinatewise Gaussianization

Jinyuan Chang, Ye Du, Jing He, Qiwei Yao

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. The key idea is simple, i.e., we first transform each component variable to the standard normal via …

cn, gb (code pays fourni par la source)

JAB1/CRL4B complex represses PPARG/ACSL5 expression to promote breast tumorigenesis

Ting Hu, Tianyu Ma, Miaomiao Huo, Jiaxiang Liu et autres

Fatty acid metabolism is critical for tumor progression, supplying bioenergetic and biosynthetic substrates to rapidly proliferating cancer cells. However, the precise mechanisms by which fatty acid metabolism influences breast cancer progression remain unclear. In this study, we aimed to explore the molecular …

cn, us (code pays fourni par la source)

Machine learning-assisted photoelectrochemical biosensor based on DNA-AgNCs nanowires for exosomal lncRNA intelligent diagnosis

Wen‐Chao Geng, Jiangying Ji, Zhiyi Yan, Fan Wu et autres

cn (code pays fourni par la source)

Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection

Zongxin Shen, Yanyong Huang, Dongjie Wang, Jinyuan Chang et autres

Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received growing attention in recent years. Despite their promising performance, existing methods face three key challenges: 1) by focusing solely on the …

A New Paradigm of Large-model Collaborative Digital-intelligent Decision-making：Key Mechanisms and Future Prospects

Gang Kou, Xingtong Chen, Xin Wang, Jinyuan Chang

The rapid development of large models is driving decision science from experience-driven approaches toward a new intelligent decision-making paradigm based on human–machine collaboration. This paper first reviews the evolutionary path of decision-making systems，which has progressed from experience-driven and data-driven models to human-machine …

Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning

Zongxin Shen, Yanyong Huang, Bin Wang, Jinyuan Chang et autres

Multi-view unsupervised feature selection (MUFS) has recently received increasing attention for its promising ability in dimensionality reduction on multi-view unlabeled data. Existing MUFS methods typically select discriminative features by capturing correlations between features and clustering labels. However, an important yet underexplored question …