论文检索

依赖删失生存模型中回归参数的界AI 解读

Abstract We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The model is partially identified, and we obtain bounds on the covariate effects which are allowed to be time-dependent. Moreover, these bounds can be interpreted as classical confidence intervals and are obtained by aggregating information in the conditional Peterson bounds over the…

全局非正曲率空间上的分位数AI 解读

Abstract We propose a notion of geometric quantiles on Hadamard spaces, or global non-positive curvature spaces. After providing some definitions and basic properties, including scaled isometry equivariance and a necessary condition on the gradient of the quantile loss function, we investigate asymptotic properties, such as strong consistency and joint asymptotic normality. We provide a detailed description of how to compute quantiles using a gradient descent algorithm in…

隐私约束下的 Knockoff 推断AI 解读

Abstract Model-X knockoff framework offers a model-free variable selection method that ensures finite-sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. We propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection…

多重实验的潜力：识别潜在结果的联合分布AI 解读

Abstract Typical causal effects are defined based on the marginal distribution of potential outcomes. However, many real-world applications require causal estimands involving the joint distribution of potential outcomes to enable nuanced treatment evaluation and selection. In this article, we propose a novel framework for identifying and estimating the joint distribution of potential outcomes using multiple experimental datasets. We introduce the assumption of…

再随机化与分层再随机化下灵活协变量调整的渐近推断AI 解读

Abstract Rerandomization is an effective treatment allocation procedure to control for baseline covariate imbalance. For estimating the average treatment effect, rerandomization has been previously shown to improve the precision of the unadjusted and the linearly adjusted estimators over simple randomization without compromising consistency. However, it remains unclear whether such results apply more generally to the class of M-estimators, including the g-computation formula…

存在缺失数据时的深度学习AI 解读

Abstract In the context of multivariate nonparametric regression with missing covariates, we propose pattern embedded neural networks (PENNs), which can be applied in conjunction with any existing imputation technique. In addition to a neural network trained on the imputed data, PENNs pass the vectors of observation indicators through a second neural network to provide a compact representation. The outputs are then combined in a third neural network to produce final…

Markov 状态切换扩散的精确贝叶斯推断AI 解读

Abstract We develop the first exact Bayesian methodology for the problem of inference in discretely observed regime switching diffusions. Switching diffusion models extend ordinary diffusions by allowing for jumps in instantaneous drift and volatility. The jumps are driven by a latent, continuous-time Markov switching process. We address the problem through an MCMC and an MCEM algorithm that target the exact posterior of diffusion parameters and the latent regime process.…

作者对《作为线性回归的增广平衡权重》讨论的回复AI 解读

David Bruns-Smith, Oliver Dukes, Avi Feller, Elizabeth L Ogburn; Authors’ reply to the Discussion of ‘Augmented balancing weights as linear regression' by

基于最优传输的多元分布无关检验的 Pitman 效率下界AI 解读

Abstract The Wilcoxon rank sum test is one of the most popular distribution-free two-sample tests for univariate data. Among the important reasons for their popularity are the striking results of Hodges–Lehmann and Chernoff–Savage, where the authors show that the asymptotic (Pitman) relative efficiency of Wilcoxon’s test compared to Student’s t-test, never falls below 0.864 (with identity score) and 1 (with Gaussian score), respectively. Motivated by these results, we…

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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…