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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...
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...
Heavy-tailed distribution theory, alongside with extreme value theory, provide a framework and an indispensable set of tools aiming to analyze the data's atypical behaviors. The eld is driven by and ...
Document classification is an area of great importance for which many clas-sification methods have been well developed. However, most of these methods cannot generate time-dependent classification rul...
We use Klee’s Dehn-Sommerville relations and other results on face numbers of homology manifolds without boundary to (i) prove Kalai’s conjecture providing lower bounds on the f-vectors of an even-dim...
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties.We develop efficient algorithms for fitting these models when the numbe...
In this paper, motivated by the idea from Courtois and Denuit et al.(2006), we derive extremal distribution for the discrete 5-convex stochastic ordering. As applications,We improve lower and upper bo...
Abstract: We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain. The constants in this inequality are explicit and depend ...
Abstract: In this paper, we introduce a new class of estimators of the Hurst exponent of the fractional Brownian motion (fBm) process. These estimators are based on sample expectiles of discrete varia...
Abstract: The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities....
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding cri...
Define a chi-square random field on a multi-dimensional lattice points index set with a direct-product covariance structure, and consider the distribution of the maximum of this random field. We provi...
Understanding of normal and pathological brain function requires the identification and localization of functional connections between specialized regions. The availability of high time resolution sig...

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