Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Zhu, Xuening"'
Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i
Externí odkaz:
http://arxiv.org/abs/2410.04028
The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR). Previous methods focused on inserting virtual objects using CAD models or real objects from single-view images, resulting in highly lim
Externí odkaz:
http://arxiv.org/abs/2406.14806
Multi-relational networks among entities are frequently observed in the era of big data. Quantifying the effects of multiple networks have attracted significant research interest recently. In this work, we model multiple network effects through an au
Externí odkaz:
http://arxiv.org/abs/2406.03296
The Gini coefficient is an universally used measurement of income inequality. Intersectoral GDP contributions reveal the economic development of different sectors of the national economy. Linking intersectoral GDP contributions to Gini coefficients w
Externí odkaz:
http://arxiv.org/abs/2405.07408
While the Vector Autoregression (VAR) model has received extensive attention for modelling complex time series, quantile VAR analysis remains relatively underexplored for high-dimensional time series data. To address this disparity, we introduce a tw
Externí odkaz:
http://arxiv.org/abs/2404.18732
Autor:
Li, Xuetong, Gao, Yuan, Chang, Hong, Huang, Danyang, Ma, Yingying, Pan, Rui, Qi, Haobo, Wang, Feifei, Wu, Shuyuan, Xu, Ke, Zhou, Jing, Zhu, Xuening, Zhu, Yingqiu, Wang, Hansheng
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three ca
Externí odkaz:
http://arxiv.org/abs/2403.11163
Autor:
Zeng, Qianhan, Zhu, Yingqiu, Zhu, Xuening, Wang, Feifei, Zhao, Weichen, Sun, Shuning, Su, Meng, Wang, Hansheng
Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose an improved Naive Bayes method
Externí odkaz:
http://arxiv.org/abs/2304.06292
Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners often suffer from limited computational resources. In most cases, they do
Externí odkaz:
http://arxiv.org/abs/2304.06231
Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the foll
Externí odkaz:
http://arxiv.org/abs/2304.02269
Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of biological mechanism. Although numerous approaches have been developed
Externí odkaz:
http://arxiv.org/abs/2303.06945