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高维极值图结构学习AI 解读

Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal variograms, for which we prove non-asymptotic concentration bounds that hold under general domain of attraction conditions. For the popular class of Hüsler--Reiss models, we propose a majority voting algorithm for learning the underlying graph from data through $L^1$ regularized optimization. Our…

未观测混杂下预测算法的稳健设计与评估AI 解读

Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected…

作为组织问题的国家能力AI 解读

Abstract We investigate how technologies that reduce the costs of monitoring by central authorities have shaped the historical transition from small patrimonial states to large bureaucratic organizations. Our analysis is based on a novel dataset that traces changes in the organizational structure and geographic presence of the U.S. federal government over the 19th century. To identify causal effects, we develop a new identification strategy that exploits the expansion of the…

通过类别分布近似实现个体疫情模型的可扩展校准AI 解读

Traditional compartmental models capture population-level dynamics but fail to characterize individual-level risk. The computational cost of exact likelihood evaluation for partially observed individual-based models, however, grows exponentially with the population size, necessitating approximate inference. Existing sampling-based methods usually require multiple simulations of the individuals in the population and rely on bespoke proposal distributions or summary…

López de Prado 与 Porcu 对《组合回归》的讨论意见AI 解读

Marcos López de Prado, Emilio Porcu; López de Prado and Porcu’s contribution to the Discussion of ‘Regression by compositio’ by Farewell et al, Journal of

Garib Nath Singh 对《通过组合进行回归》的讨论意见AI 解读

Garib Nath Singh; Professor Garib Nath Singh’s contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statis

Safaa K Kadhem 对《通过组合进行回归》的讨论意见AI 解读

Safaa K Kadhem; Safaa K Kadhem's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical Society

对Farewell等《构成回归》的致谢附议与讨论AI 解读

Peter McCullagh; Seconder of the vote of thanks to Farewell et al. and contribution to the Discussion of Regression by composition', Journal of the Royal S

赵睿轩等对《组合回归》的讨论意见AI 解读

Ruixuan Zhao, Oliver Dukes, Linbo Wang, Lin Liu; Ruixuan Zhao, Oliver Dukes, Linbo Wang, and Lin Liu’s contribution to the Discussion of ‘Regression by com

Gaffi、Legramanti 与 Rimella 对《通过组合进行回归》的讨论AI 解读

Francesco Gaffi, Sirio Legramanti, Lorenzo Rimella; Gaffi, Legramanti, and Rimella’s contribution to the Discussion of ‘Regression by Composition’ by Farew

严景新、刘林等对《组合回归》的讨论AI 解读

Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, Linbo Wang; Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, and Linbo Wang’s contribution to the Discussion o

Cheng等对Farewell等《组合回归》的讨论AI 解读

Kaiming Cheng, Rong Jiang, Jiangfeng Wang, Keming Yu; Cheng et al's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journa

田茂再、熊伟、马少培与梁金文对《组合回归》的讨论AI 解读

Maozai Tian, Wei Xiong, Shaopei Ma, Jinwen Liang; Maozai Tian, Wei Xiong, Shaopei Ma, and Jinwen Liang's Contribution to the Discussion of ‘Regression by C

Archer Gong Zhang 对《组合回归》的讨论AI 解读

Archer Gong Zhang; Archer Gong Zhang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical So

Andi Wang 对 Farewell 等人《通过组合进行回归》的讨论AI 解读

Andi Q Wang; Andi Wang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical Society Series B

董梅、王林波、刘林与Oliver Dukes对“复合回归”的讨论意见AI 解读

Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes; Mei Dong, Linbo Wang, Lin Liu, and Oliver Dukes's contribution to the Discussion of ‘Regression by composition

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Li、van Dyk与Autenreith对《组合回归》的讨论AI 解读

Si-Yang Li, David A van Dyk, Maximilian Autenrieth; Si-Yang Li, David van Dyk and Maximilian Autenreith's contribution to the Discussion of ‘Regression by

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带函数型协变量的随机区块模型：变分非参数推断AI 解读

We propose a functional stochastic block model whose vertices involve functional data information. This new model extends the classic stochastic block model with vector-valued nodal information, and finds applications in real-world networks whose nodal information could be functional curves. Examples include international trade data in which a network vertex (country) is associated with the annual or quarterly GDP over a certain time period, and MyFitnessPal data in which a…

通过分割预测检验实现校准的模型诊断AI 解读

Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practical use (e.g., due to requiring model-specific derivations). We propose split predictive checks (SPCs) as a simple, general-purpose class of predictive checks that maintain the usability of posterior predictive checks while directly…

自动调参的回火哈密顿蒙特卡洛：高维多峰分布采样AI 解读

Abstract Hamiltonian Monte Carlo (HMC) is widely used for sampling from high-dimensional target distributions with densities known up to proportionality. While HMC exhibits favourable scaling properties in high dimensions, it struggles with strongly multimodal distributions. Tempering methods are commonly used to address multimodality, but they can be difficult to tune, especially in high-dimensional settings. In this study, we propose a method that combines tempering with…