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摘要暂缺

Doss 与 Huling 对《增广平衡权重作为线性回归》的讨论AI 解读

Abstract Doss and Huling’s contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al.

Zhu Shen 与 José R. Zubizarreta 对《增广平衡权重作为线性回归》的讨论AI 解读

Zhu Shen, José R Zubizarreta; Zhu Shen and José R. Zubizarreta’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Br

田茂再、刘硕与谭孟对《作为线性回归的增广平衡权重》的讨论AI 解读

Maozai Tian, Shuo Liu, Tan Meng; Tian, Liu and Tan's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et

Rotnitzky、Smucler 与 Robins 对《作为线性回归的增广平衡权重》的讨论AI 解读

Andrea Rotnitzky, Ezequiel Smucler, James M Robins; Rotnitzky, Smucler and Robins contribution to the Discussion of ‘Augmented balancing weights as linear

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王江峰、江荣、俞克明对《作为线性回归的增广平衡权重》的讨论AI 解读

Jiangfeng Wang, Keming Yu, Rong Jiang; Jiangfeng Wang, Keming Yu and Rong Jiang's contribution to the Discussion of ‘Augmented balancing weights as linear

Shan、Ying 与 Zhao 对《作为线性回归的增广平衡权重》的讨论AI 解读

Jiawei Shan, Chao Ying, Jiwei Zhao; Shan, Ying and Zhao’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smi

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多重随机化设计：存在干扰时的估计与推断AI 解读

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple…

Tiwari与Basu对《作为线性回归的增广平衡权重》的讨论AI 解读

Supriya Tiwari, Pallavi Basu; Supriya Tiwari and Pallavi Basu's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bru

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控制错误发现率的联邦特征选择AI 解读

Abstract Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this…

面向稀疏模式异质性的多任务学习：统计与计算视角AI 解读

Abstract We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets (‘tasks’). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging (1) coefficient supports,…

Mondrian 随机森林的统计推断AI 解读

Abstract Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondrian random forest, in which the underlying constituent trees are constructed via a Mondrian process. We give precise bias and variance characterizations, along with a Berry–Esseen-type central limit theorem, for the Mondrian random forest regression estimator. By combining these results…

弱识别函数的强识别泛函推断AI 解读

Abstract In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and analysis of missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g. average causal effects) of nuisance functions (e.g. NPIV regression) defined by conditional moment restrictions. These nuisance functions are often weakly identified, meaning the…

Abstract Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency…

带颜色约束的高斯有向无环图模型AI 解读

Abstract We study submodels of Gaussian directed acyclic graph (DAG) models defined by partial homogeneity constraints imposed on the model error variances and structural coefficients. We represent these models with coloured DAGs and investigate their properties for use in statistical and causal inference. Local and global Markov properties are provided and shown to characterize the coloured DAG model. Additional properties relevant to causal discovery are studied, including…

带人工动态的自组织状态空间模型AI 解读

Abstract We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter θ. A popular idea to address this problem consists of incorporating θ in the state of the system and allowing its time evolution, modelled as a Markov chain (θt)t≥1. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a…

分布式差分隐私约束下非参数分类的极小极大与自适应迁移学习AI 解读

Abstract This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source…

风险最小化参数的广义通用推断AI 解读

Abstract A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications. The present paper considers a very common case in machine learning, where the quantity of interest is the…

作为线性回归的增广平衡权重AI 解读

Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single…