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Causal inference is a permanent challenge topic in statistics, data science, and many other applied fields. Existing machine learning methods often focus on the correlations in the data and ignore the...
Given a quantum observable and a state, one can construct a classical observable, such that it is the optimal estimate of the quantum observable, in the sense of minimum variance. We demonstrate how t...
这是一个大数据与人工智能的时代,催生了各种各样大规模商务新场景。新场景常常呈现出:大规模、大数据、大模型的特点。这些特点对统计学的理论与计算方法都提出了新挑战,也提供了新机遇。对此,我将通过三个具体的真实案例分享,希望从中能够总结典型的统计学科学问题,洞察未来可能的研究机遇。这三个案例分别来自:金融科技、视频直播、和智慧零售三个不同商务场景。其中科技金融案例将展示如何通过二维码聚合支付数据助力面向...
The explosion of spatiotemporal data in the physical world requires new deep learning tools to model complex dynamical systems.
Best subset selection is an important problem in regression analysis,which has many applications in computer science and medicine. However, the existing best subset selection methods have some limitat...
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest ris...
We study multi-period inventory control systems in which managers face seasonal demands with unknown distributions and make inventory decisions based on past demand data. It can be shown that a data-d...
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
2022年12月28日,西安交通大学第八届丝绸之路青年学者研讨会数学与统计学院分论坛于线上成功召开。学院领导、系主任、相关系所教师、学生以及来自法国索邦大学、香港科技大学、匹兹堡大学、清华大学、北京大学等高校的六位青年学者出席本次论坛。论坛由院长孙建永教授主持。
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Tim...
In experimental design, a common problem seen in practice is when the result includes one binary response and multiple continuous responses. However, this problem receives scant attention. Most studie...
We propose a sequential method for implementing reconstruction regression. With a space-filling design of a small sample size, we use GP interpolation-based reconstruction regression to build initial ...
在非线性期望下,随机过程与极限理论表现出了与经典概率空间完全不同的性质。比如,连续非增鞅不再是平凡过程;独立同分布随机变量序列的经验均值不再收敛到某个常数。本报告从Feynman-Kac公式出发,介绍非线性期望理论建立的动机,近期的主要进展,以及目前尚未解决的一些具有挑战性的问题。
This paper proposes a method to accelerate calculation for generalized linear models in big data. We separate the covariates of full model into several groups to build candidate models. In the process...

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