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依赖结构下的聚类后推断AI 解读

Recent work by Gao et al. (2024) has laid the foundations for post-clustering inference, establishing a theoretical framework allowing to test for differences between means of estimated clusters. Additionally, they studied the estimation of unknown parameters while controlling the selective type I error. However, their theory was developed for independent observations identically distributed as p-dimensional Gaussian variables, where the parameter estimation could only be…

MOSAiC：多站点压缩风险函数的一次性聚合AI 解读

Multi-center research has strengthened the generalizability and reproducibility of scientific findings, yet multi-site inference in modern distributed research networks remains constrained by privacy regulations, heterogeneous site characteristics, and the logistical burden of human-in-the-loop, multi-round communication. In this work, we distill these operational constraints into four mathematical requirements for practical distributed inference and introduce MOSAiC, a…

用匹配自适应随机化推断重新评估激素替代疗法对心脏病的影响AI 解读

Matching is an appealing way to design observational studies because it mimics the data structure produced by stratified randomized trials, pairing treated individuals with similar controls. After matching, inference is often conducted using methods tailored for stratified randomized trials in which treatments are permuted within matched pairs. However, in observational studies, matched pairs are not predetermined before treatment; instead, they are constructed based on…

We estimate wage cyclicality using the user cost of labor (UCL), a measure of marginal labor cost. We propose a novel methodology to correct composition bias from cyclical jobto-job upgrading by exploiting school-to-employment flows. We implement the methodology using rich Japanese wage data. The estimated UCL is strongly procyclical, with a semi-elasticity of 2.52 with respect to the unemployment rate, which is 1.6 and 2.3 times as large as that of the new-hire and average…

概率预测中适当损失函数的非对称惩罚AI 解读

Abstract Accurately forecasting the probability distribution of phenomena of interest is a classic and increasingly widespread goal in statistics and decision theory. In comparison to point forecasts, probabilistic forecasts aim to provide a more complete and informative characterization of the target variable. This endeavour is only fruitful, however, if a forecast is ‘close’ to the distribution it attempts to predict. The role of a loss function, also known as a scoring…

针对协变量完全缺失的增广迁移回归学习AI 解读

Abstract Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing-data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this…

高维异质中介分析的检验功效增强AI 解读

Mediation models are widely used in many scientific areas to understand how an exposure variable impacts an outcome variable through an intermediate variable. In this article, we develop powerful inferential methods to analyze mediation effects in high-dimensional contexts with intercorrelated potential mediators in models with linear and generalized linear outcomes. We draw attention to heterogeneous mediation, and show that existing testing approaches based on the total…

利用星际边界探测器数据分离带状与全球分布通量的统计方法AI 解读

NASA’s Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that can provide insight into the heliosphere boundary between our solar system and interstellar space. Using these data, scientists can construct maps of the ENA intensities (often, expressed in terms of flux) observed in all directions. The ENA flux observed in these maps is believed to come from at least two distinct sources: one source which manifests as a ribbon of…

通过潜在空间分布匹配进行半监督生成学习AI 解读

We introduce Latent Space Distribution Matching (LSDM), a novel framework for semi-supervised generative modeling of conditional distributions. LSDM operates in two stages: (i) learning a low-dimensional latent space from both paired and unpaired data, and (ii) performing joint distribution matching in this space via the 1-Wasserstein distance, using only paired data. This two-step approach minimizes an upper bound on the 1-Wasserstein distance between joint distributions,…

平均处理效应局部化：合成控制中的投影方法AI 解读

Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this article, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear…

通过模型平均实现多层网络迁移学习的框架AI 解读

Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation…

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协变量自适应随机实验中混合效应模型的统计推断AI 解读

Recent clinical studies increasingly involve a long factor with many levels, e.g., investigation sites, resulting in a large number of strata that must be accounted for either through design or subsequent analysis. This complication has prompted concerns from the U.S. Food and Drug Administration (FDA, 2023) regarding the adequacy of standard statistical methods, whose performance may deteriorate, and their properties become unclear when the number of strata is relatively…

面向重尾数据的高维切片逆 Kendall 秩相关估计AI 解读

Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under a supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall’s tau Estimation (SIKE) designed for analyzing…

集体家庭消费中的规模经济AI 解读

ABSTRACT Browning et al. (2013) model collective‐household economies of scale in goods consumption by having each good be partly shared, instead of each good being public or private. We modify their model to achieve simple point identification and estimation of the economies of scale of consumption of each good, and we provide a new index of household level economies of scale. Our model has a linear form, permits households of varying composition, including children, and…

用广义加性密度比模型处理正例与未标记数据AI 解读

Abstract This paper focuses on learning from positive and unlabelled (PU) data, where only some positives are labelled and the rest are mixed with negatives. Classical exponential tilting models guarantee identifiability by imposing a linear structure, but they can be severely misspecified when the true relationships are nonlinear. We propose a generalized additive density-ratio framework that retains identifiability while allowing nonlinear and feature-specific effects. The…

离散空间随机场的有向图模型混合AI 解读

Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian setting when full posterior inference is desired. As an alternative, we propose a novel statistical modeling framework for this data setting, namely a mixture of directed graphical models (MDGMs). The components of the mixture, directed graphical models, can be represented by directed acyclic…

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非平凡协整的检验AI 解读

Cointegration is pivotal in analyzing long-run equilibrium relationships among economic variables. Traditional cointegration models have been effective in handling mixed-order integrated variables, but they can lead to misleading conclusions from an equilibrium perspective if trend stationary observables are involved. This type of variable leads to the so-called trivial cointegration, which might falsely suggest a long-run relationship where none exists. Testing for…

双重因子结构下高维函数型时间序列的协方差函数估计AI 解读

We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor structure is imposed on the observed functional processes, whereas a low-dimensional version (via series approximation) is assumed for the latent functional factors. We extend the classic principal component analysis technique for estimating a low-rank structure to the estimation of a large covariance matrix of…

高维均值向量的分布无关收缩估计AI 解读

We introduce shrinkage estimators of the sample mean vector in high dimension. Our estimators share desirable properties relative to existing methods: they are distribution-free, consider bona-fide target estimators, and use simple estimators of the shrinkage intensities independent of the precision matrix which are L2 consistent in high dimension. Unlike existing estimators that impose that the whitened data be an i.i.d. matrix, we only impose i.i.d. across sample…

无条件异方差下基于 LAD 的自适应 Bootstrap 单位根检验AI 解读

This paper studies unit root testing based on least absolute deviations (LAD) regression under unconditional heteroscedasticity. We first derive asymptotic properties of the LAD estimator under both unit root and local-to-unity settings in the presence of unconditional heteroscedasticity and weak dependence. The results show that the limiting distribution of the LAD estimator (and thus the derived test statistics) is closely associated with unknown heteroscedasticity. To…

非线性结构模型中边际效应分布与分位数的面板估计：垃圾食品需求应用AI 解读

In this paper, we propose a framework to estimate the distribution of marginal effects in a general class of structural models that allow for very general nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel and use differences in outcome variables of nearby subpopulations to obtain the…

如何比较 Copula 预测？AI 解读

This paper lays out a principled approach to compare copula forecasts via strictly consistent scores. We first establish the negative result that, in general, copulas fail to be elicitable, implying that copula predictions alone do not allow for meaningful comparisons. A notable exception is on Fréchet classes, that is, when the marginals are given and identical across all elements of the class, in which case we give suitable scores for the copula forecast comparison. As a…

异质性部分干扰下观察性研究处理效应的半参数估计AI 解读

In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous…

不同频率预测的统计比较AI 解读

Current forecast evaluation tests can only assess forecasts generated at the same frequency. However, in real-world scenarios, predictions for the same economic variable may be made at different frequencies. The existing literature lacks statistical tests for evaluating such forecasts. This paper introduces a new evaluation test for comparing these forecasts. We propose a two-sample t-type test designed to test the equality of means between two potentially correlated time…