Search Results - "Chen, Elynn"

Community network auto-regression for high-dimensional time series by Chen, Elynn Y., Fan, Jianqing, Zhu, Xuening

“…Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector…”
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Statistical Inference for High-Dimensional Matrix-Variate Factor Models by Chen, Elynn Y., Fan, Jianqing

“…This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the…”
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Constrained Factor Models for High-Dimensional Matrix-Variate Time Series by Chen, Elynn Y., Tsay, Ruey S., Chen, Rong

“…High-dimensional matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To…”
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Semi-parametric tensor factor analysis by iteratively projected singular value decomposition by Chen, Elynn Y, Xia, Dong, Cai, Chencheng, Fan, Jianqing

“…Abstract This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank…”
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Identification and estimation of threshold matrix‐variate factor models by Liu, Xialu, Chen, Elynn Y.

“…Motivated by the growing availability of complex time series observed in real applications, we propose a threshold matrix‐variate factor model, which…”
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Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow by Chen, Elynn Y., Chen, Rong

“…International trade research plays an important role to inform trade policy and shed light on wider economic issues. With recent advances in information…”
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Reinforcement Learning in Latent Heterogeneous Environments by Chen, Elynn Y., Song, Rui, Jordan, Michael I.

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High-Dimensional Tensor Discriminant Analysis with Incomplete Tensors by Chen, Elynn, Han, Yuefeng, Li, Jiayu

“…Tensor classification is gaining importance across fields, yet handling partially observed data remains challenging. In this paper, we introduce a novel…”
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High-Dimensional Tensor Classification with CP Low-Rank Discriminant Structure by Chen, Elynn, Han, Yuefeng, Li, Jiayu

“…Tensor classification has become increasingly crucial in statistics and machine learning, with applications spanning neuroimaging, computer vision, and…”
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Factor Augmented Matrix Regression by Chen, Elynn, Fan, Jianqing, Zhu, Xiaonan

“…We introduce \underline{F}actor-\underline{A}ugmented \underline{Ma}trix \underline{R}egression (FAMAR) to address the growing applications of matrix-variate…”
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Statistical Inference for High-Dimensional Matrix-Variate Factor Model by Chen, Elynn Y, Fan, Jianqing

“…Journal of the American Statistical Association, 2021 This paper considers the estimation and inference of the low-rank components in high-dimensional…”
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Data-Driven Knowledge Transfer in Batch $Q^$ Learning by Chen, Elynn, Chen, Xi, Jing, Wenbo

“…In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate…”
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Tensor-view Topological Graph Neural Network by Wen, Tao, Chen, Elynn, Chen, Yuzhou

“…Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph…”
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TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting by Kong, Linghang, Chen, Elynn, Chen, Yuzhou, Han, Yuefeng

“…Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate…”
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Tensor-Fused Multi-View Graph Contrastive Learning by Wu, Yujia, Mo, Junyi, Chen, Elynn, Chen, Yuzhou

“…Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from…”
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Conditional Prediction ROC Bands for Graph Classification by Wu, Yujia, Yang, Bo, Chen, Elynn, Chen, Yuzhou, Zheng, Zheshi

“…Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce…”
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Exploring causal effects of hormone- and radio-treatments in an observational study of breast cancer using copula-based semi-competing risks models by Yu, Tonghui, Peng, Mengjiao, Cui, Yifan, Chen, Elynn, Chen, Chixiang

“…Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon,…”
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Advancing Information Integration through Empirical Likelihood: Selective Reviews and a New Idea by Chen, Chixiang, Liang, Jia, Chen, Elynn, Wang, Ming

“…Information integration plays a pivotal role in biomedical studies by facilitating the combination and analysis of independent datasets from multiple studies,…”
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Distributed Tensor Principal Component Analysis by Chen, Elynn, Chen, Xi, Jing, Wenbo, Zhang, Yichen

“…As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and…”
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Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models by Chen, Elynn, Chen, Xi, Gao, Lan, Li, Jiayu

“…In the evolving landscape of digital commerce, adaptive dynamic pricing strategies are essential for gaining a competitive edge. This paper introduces novel…”
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