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企业自动化后，员工会怎样？AI 解读

Abstract We estimate the impact of firm-level automation on individual worker outcomes by combining Dutch microdata with a direct measure of automation expenditures covering all private nonfinancial sector firms. Using a novel difference-in-differences event-study design leveraging lumpy investment, we find that automation increases the probability of incumbent workers separating from their employers. Workers experience a five-year cumulative wage income loss of 9% of one…

《数据裂分：拆分单个数据点》讨论：数据裂分的若干渐近结果AI 解读

This comment was presented as part of the JSM 2024 JASA Theory and Methods invited session Data Fission: Splitting a Single Data Point.1 Leiner et al. (2025) introduce the data fission technique, w...

通过数据裂分进行经验贝叶斯估计AI 解读

We congratulate Leiner, Duan, Wasserman, and Ramdas on a stimulating article that joins an elegant method to a compelling application. Their article focuses primarily on applications to selective i...

数据裂分与抽样设计：讨论AI 解读

Leiner et al. offers a new paradigm for post-selection inference while connecting to the long-standing importance of conditioning in likelihood inference. By injecting noise to the data, the transf...

数据裂分评论：无监督学习中的聚类后推断与去偏挑战AI 解读

This article introduced data fission (Leiner et al. 2023), a valuable methodology for post-selection inference. While the article focused on the supervised setting, we explore its potential for uns...

利用重采样数据进行弱假设数据裂分AI 解读

Gaussian additive noise data fission is described in Tian and Taylor (2018) and expanded in Rasines and Young (2022). This current work, Leiner et al. (2025), expands greatly on this idea by creati...

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eDNAPlus：基于 DNA 的生物多样性监测统一建模框架AI 解读

DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read “DNA barcode” sequences, generating count data indicating the number of times each DNA barcode was…

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加性协方差矩阵模型：英国区域电力净需求建模AI 解读

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain’s…

立法机构的操作化研究：贝叶斯方法的方法论与实证视角AI 解读

This article extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as Latin America. First, we provide a detailed description of the Bayesian ideal point estimator. Next, we propose a new taxonomy to synthesize and frame technical developments and applications associated with the estimator in the context of the United States Congress and Latin American…

利用排序加权平均处理效应评价处理优先规则AI 解读

There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules. RATE metrics are agnostic as to how the prioritization rules were derived, and only assess how well they identify…

高维特征下的双重稳健增广模型准确度迁移推断AI 解读

Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel Doubly Robust Augmented Model Accuracy Transfer Inferen Ce (DRAMATIC) method for point and interval…

用于地理空间数据的神经网络AI 解读

Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, encoding the spatial dependence. While nonlinear machine learning algorithms like neural networks are increasingly being used for spatial analysis, current approaches depart from the model-based setup and cannot explicitly incorporate spatial covariance. We propose NN-GLS, embedding neural…

有理克里金AI 解读

This article proposes a new kriging that has a rational form.It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging.Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework.A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases.Extensive simulations…

协变量分布迁移下的分布无关预测区间及因果推断应用AI 解读

Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these…

通过贝叶斯线性规划控制信号发现与定位AI 解读

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise in any application where signals cannot be perfectly localized, such as…

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基于平滑欧拉示性数变换的随机形状建模与统计推断AI 解读

In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of…

图对齐随机分区模型（GARP）AI 解读

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by…

基于Tyler M估计量的大维形状矩阵检验AI 解读

Tyler's M estimator, as a robust alternative to the sample covariance matrix, has been widely applied in robust statistics. However, classical theory on Tyler's M estimator is mainly developed in the low-dimensional regime for elliptical populations. It remains largely unknown when the parameter of dimension p grows proportionally to the sample size n for general populations. By utilizing the eigenvalues of Tyler's M estimator, this article develops tests for the identity…

高维矩阵值序列的建模与学习AI 解读

We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basis vector. RaDFaM contributes a novel class of low-rank latent structure that makes tradeoff between signal intensity and dimension reduction from the perspective of tensor subspace. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued…

高维因子模型中基于自助样本协方差矩阵的共同因子个数检验AI 解读

This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked…

共形预测集的选择与聚合AI 解读

Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In the context of point prediction,…

Sharp-SSL：用于半监督学习的选择性高维轴对齐随机投影AI 解读

We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is to identify important variables for distinguishing between the classes; existing low-dimensional methods can then be applied for final class assignment. To this end, we score projections according to their class-distinguishing ability; for…