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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...
Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is a...
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...
中国矿业大学数学学院于2016年9月成立,行政建制最早追溯到1950年的焦作工学院时期的“数理研究指导组”,历经基础部、应用数学力学部、应用数学力学系和理学院。 学院在编在岗教职工109名,其中教师岗94人,管理岗11人,实验室岗3人,工勤岗1人。专任教师中教授21名、副教授42名,博士生导师13名、硕士生导师40名。目前,在校生规模为895名,其中博士研究生35名、硕士研究生151名、本科生7...
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...
差异性分析是生物医学研究中的一个重要问题。它旨在通过比较两组或多组样本数据之间的差异来识别影响复杂疾病或性状的基因、生物通路、微生物等。针对人类微生物组和水稻耐寒性的差异性分析问题,提出了自适应的分组聚合检验方法,给出了统计显著性的快速算法及其统计性质,利用多尺度分析方法有效地整合水稻耐寒性的两个性状,提出了群体分层校正方法并将其应用到水稻耐寒性的数据中,发现并验证了与水稻耐寒性相关联的基因COL...
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...
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 ...
This paper is concerned with the factorization and equivalence problems of multivariate polynomial matrices. We present some new criteria for the existence of matrix factorizations for a class of mult...

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