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检测异质组织跨样本空间表达变异的灵活贝叶斯框架AI 解读

Spatial transcriptomics measures gene expression alongside the spatial coordinates of each capture spot or cell across tissue samples. The detection of spatially variable (SV) genes, whose expression exhibits systematic spatial variation, enables the delineation of functional tissue domains and the identification of region-specific transcriptional changes that underlie disease heterogeneity. However, empirical evidence from spatial transcriptomics data indicates that…

超越固定截断时点：临床试验中的自适应限制平均生存时间方法AI 解读

Summary Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook…

最优治疗规则下平均结局的最优推断AI 解读

Abstract When an optimal treatment regime (OTR) is considered, we need to evaluate the OTR in a valid and efficient way. The classical inference applied to the mean outcome under OTR, assuming the OTR is the same as the estimated OTR, might be biased when the regularity assumption that OTR is unique is violated. Although several methods have been proposed to allow nonregularity in such inference, its optimality is unclear due to challenges in deriving semiparametric…

一族相干风险度量：在险价值的无限加权平均AI 解读

Value-at-Risk (VaR) remains one of the most widely used risk measures in finance due to its simplicity, interpretability, and regulatory prominence, particularly within the Basel framework. However, VaR is not a coherent risk measure, as it generally fails to satisfy the subadditivity property. This paper demonstrates that, although finite weighted combinations of VaR-type functionals do not necessarily yield coherent risk measures, appropriately constructed infinite…

乘法工具变量模型AI 解读

Summary The instrumental variable (IV) design is a common approach to address hidden confounding bias. For validity, an IV must impact the outcome only through its association with the treatment. In addition, IV identification has required a homogeneity condition such as monotonicity or no unmeasured common effect modifier between the additive effect of the treatment on the outcome, and that of the IV on the treatment. In this work, we introduce the Multiplicative…

存在少报的有序选择模型中的变化中的变化AI 解读

We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…

主分层可忽略性下基于 U 统计量的主分层推断AI 解读

Abstract Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries…

Abstract Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effective new estimators for causal functionals. We establish several novel theoretical results for them, building on recent work. We provide a structure-agnostic error analysis, which holds with generic nuisance functions and estimators. Then, we propose n-consistent DCDR estimators with undersmoothed local polynomial regression and k-Nearest…

条件可分离效应的近端因果推断AI 解读

Abstract Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the…

网络干扰下修正处理政策的因果效应AI 解读

Abstract Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention—induced modified treatment policies—which we show…

利用受限得分检验推断函数值参数AI 解读

Abstract It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In many cases, these estimands can be expressed as the minimizer of a population risk functional. Here, we propose a…

用于变点检测的在线核 CUSUM 方法AI 解读

Abstract We present a computationally efficient online kernel Cumulative Sum method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including the Scan-B statistic, corresponding to a non-parametric Shewhart chart-type procedure. We provide accurate analytic…

基于鞅后验的贝叶斯分位数估计与回归AI 解读

Abstract Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior…

用户指定异方差参数模型下的回归有效估计AI 解读

We show that it is possible to estimate regression coefficients at least as precisely as Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and its recently proposed refinements, regardless of the user’s parametric model for heteroskedasticity. The key is to modify WLS depending on scalar target parameters, e.g., coefficients, marginal effects, predictions, etc. to obtain a targeted WLS that maximizes precision of estimation of the target given the user’s model for…

极端事件估计的分布鲁棒优化框架AI 解读

Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter a difficult bias-variance tradeoff that exacerbates especially when data size is too small, deteriorating the reliability of the tail estimation. In this paper, we study a framework based on the recently surging literature of distributionally robust…

异质分布式隐私约束下非参数回归的最优联邦学习AI 解读

This paper investigates federated learning for nonparametric regression under heterogeneous differential privacy (DP) constraints. Data is distributed across multiple servers with varying sample sizes and privacy budgets, with each server communicating privatized summaries to a central aggregator. Our work operates within the Federated Differential Privacy (FDP) framework, which generalizes both local and central DP frameworks.We establish the minimax optimal rates of…

分层空间上的非参数统计及其在对象数据分析中的应用AI 解读

Victor Patrangenaru and Daniel E. Osborne. Boca Raton, FL: Chapman & Hall/CRC Press, 2025, xv + 187 pp., $189.99(H), ISBN: 978-1-138-04313-8.Nowadays, more and more data we meet are not just simple...

可观测因子与潜在因子AI 解读

We test for temporal stability in local linear projection coefficients of observable factors on latent ones embedded in a high-dimensional vector obeying a linear factor model. The proposed test explores the fact that, under the null hypothesis, residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via Principal Component Analysis (PCA), should also be locally uncorrelated with the PCA factors. The test is fully…

设定 LASSO 与灵活的特征型资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

共同基金 alpha 的异质可预测性：稀疏聚类 GMM 方法AI 解读

Mutual fund managers' skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified by machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha's predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model this heterogeneous predictability, this paper introduces Sparse Clustering GMM, a nonparametric approach for uncovering latent fund groups. SCGMM clusters…

强度变化的重复处理下的工具变量回归AI 解读

Instrumental variable models with repeated endogenous treatments are popular in empirical research using pooled cross-sectional or short panel datasets. This paper proposes a novel semi-parametric approach that explicitly considers 1) path dependency in the contemporaneous treatment effect and 2) a direct carryover effect from past treatments. We demonstrate that when either of these new features is present, the standard two-stage least squares estimator is generally…

MCA：面向条件众数的高维众数成分分析AI 解读

The modal factor model represents a novel statistical framework for dimension reduction in high dimensional panel data analysis. It is specifically designed to capture modal factors which exert a significant influence on the conditional mode of the distribution of the observables. Statistical inference for this model is developed based on a newly proposed modal component analysis, where modal factors and their corresponding loadings are estimated through maximizing a…

含内生性的删失部分线性分位数回归AI 解读

This paper considers the estimation of a censored partial linear quantile regression model with endogeneity. Our method extends those for partial linear quantile regression (Lee, 2003, Qu et al. 2024), nonparametric quantile regression (Qu and Yoon, 2015, Belloni et al., 2019), and censored linear quantile regression with endogeneity (Chen, 2018). Our estimation method involves a tractable sequential-multiple-step procedure. We establish the uniform consistency, the uniform…

基于共形化闭合检验的集体异常检测与计数AI 解读

This paper develops a flexible distribution-free method for collective outlier detection and enumeration, designed for situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. This method builds upon recent developments in conformal inference and integrates classical ideas from other areas, including multiple testing, locally most…