Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Leng, Chenlei"'
We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor parametrisation is imposed on the observed functional processes, whereas a low-dimensi
Externí odkaz:
http://arxiv.org/abs/2401.05784
Transfer learning is an emerging paradigm for leveraging multiple sources to improve the statistical inference on a single target. In this paper, we propose a novel approach named residual importance weighted transfer learning (RIW-TL) for high-dimen
Externí odkaz:
http://arxiv.org/abs/2311.07972
Autor:
Wu, Weichi, Leng, Chenlei
Contemporary time series data often feature objects connected by a social network that naturally induces temporal dependence involving connected neighbours. The network vector autoregressive model is useful for describing the influence of linked neig
Externí odkaz:
http://arxiv.org/abs/2309.08488
Dynamic network data analysis requires joint modelling individual snapshots and time dynamics. This paper proposes a new two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, o
Externí odkaz:
http://arxiv.org/abs/2305.12643
The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due t
Externí odkaz:
http://arxiv.org/abs/2302.07533
The Gaussian graphical model is routinely employed to model the joint distribution of multiple random variables. The graph it induces is not only useful for describing the relationship between random variables but also critical for improving statisti
Externí odkaz:
http://arxiv.org/abs/2212.06931
Diversity in human capital is widely seen as critical to creating holistic and high quality research, especially in areas that engage with diverse cultures, environments, and challenges. Quantifying diverse academic collaborations and its effect on r
Externí odkaz:
http://arxiv.org/abs/2204.11713
Datasets containing both categorical and continuous variables are frequently encountered in many areas, and with the rapid development of modern measurement technologies, the dimensions of these variables can be very high. Despite the recent progress
Externí odkaz:
http://arxiv.org/abs/2112.07145
Autor:
Cai, Qizhe, Lin, Mingming, Zhang, Miao, Qin, Yunyun, Meng, Yuanlong, Wang, Jiangtao, Leng, Chenlei, Zhu, Weiwei, Li, Jie, You, Junjie, Lu, Xiuzhang
Publikováno v:
In International Journal of Cardiology 1 December 2024 416
Correlated data are ubiquitous in today's data-driven society. While regression models for analyzing means and variances of responses of interest are relatively well-developed, the development of these models for analyzing the correlations is largely
Externí odkaz:
http://arxiv.org/abs/2109.05861