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通过单峰化输运处理多峰分布：Warp-U采样与随机桥采样估计AI 解读

Monte Carlo integration is a powerful tool for scientific and statistical computation, but faces significant challenges when the integrand is a multi-modal distribution, even when the mode locations are known. This work introduces novel Monte Carlo sampling and integration estimation strategies for the multi-modal context by leveraging a generalized version of the stochastic Warp-U transformation (Wang et al., 2022). We propose two flexible classes of Warp-U transformations,…

用于改进估计与推断的贝叶斯迁移学习AI 解读

Transfer learning enhances model performance in a target population with limited samples by leveraging knowledge from related studies. While many works focus on improving predictive performance, challenges persist in statistical inference. Bayesian approaches naturally provide uncertainty quantification for parameter estimates; however, existing Bayesian transfer learning methods are typically limited to single-source scenarios or require individual-level data. We introduce…

局部隐私下非参数情境多臂老虎机的迁移学习AI 解读

Motivated by privacy concerns in sequential decision-making on sensitive data, we address the challenging problem of nonparametric contextual multi-armed bandits (MAB) under local differential privacy (LDP). Via a novelly designed LDP-compatible confidence bound, we propose an algorithm that achieves near-optimal regret performance, whose optimality is further supported by a first-seen minimax lower bound. We further consider the case of private transfer learning where…

Fréchet累积协方差网络：随机对象的深度非线性充分降维AI 解读

Nonlinear sufficient dimension reduction\citep{libing_generalSDR}, which constructs nonlinear low-dimensional representations to summarize essential features of high-dimensional data, is an important branch of representation learning. However, most existing methods are not applicable when the response variables are complex non-Euclidean random objects, which are frequently encountered in many recent statistical applications. In this paper, we introduce a new statistical…

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连续暴露的增量干预效应AI 解读

Causal inference problems often involve continuous treatments, such as dose, duration, or frequency. However, identifying and estimating standard dose-response estimands requires that everyone has some chance of receiving any level of the exposure (i.e., positivity). To avoid this assumption, we consider stochastic interventions based on exponentially tilting the treatment distribution by some parameter $δ$ (an incremental effect); this increases or decreases the likelihood…

高维谱匹配：基于普适性的协方差推断AI 解读

We propose a new spectrum matching framework based on universality for high-dimensional covariance inference. The method constructs reference distributions for a broad class of spectral statistics by generating an auxiliary Gaussian matrix whose covariance spectrum matches that of the target covariance, with validity justified by universality. This framework accommodates both known and unknown covariance settings and adapts to different inferential targets, including…

翻转干预下的纵向加权与修剪处理效应AI 解读

Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily generalize to longitudinal data and remain vulnerable to time-varying positivity violations. We extend weighting and trimming to longitudinal settings via stochastic “flip” interventions, which maintain the treatment status of subjects who would have received the target treatment, and flip others’…

基于核岭回归的条件平均处理效应迁移学习AI 解读

The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i) the source and target populations, as well as (ii) the treatment and control groups within the source. We propose a novel method for overlap-adaptive…

干扰参数满足不等式约束时的简单自适应置信区间AI 解读

Inequalities may appear in many models. They can be as simple as assuming a parameter is nonnegative, possibly a regression coefficient or a treatment effect. This paper focuses on models with one inequality and proposes an inequality-imposed confidence interval (IICI) that has particularly attractive features. The IICI is simple in that it does not require simulations or tuning parameters. Also, the IICI is adaptive to the slackness of the inequality, uniformly valid, and…

利用中心对称核进行谱密度估计中的短滞后差分AI 解读

Summary Mean-robust estimation of spectral density and long-run variance is crucial for many statistical inference procedures. However, existing methods often degrade when serially dependent data exhibit volatile, time-varying trends, or sudden jumps, particularly in small samples. While differencing and kernel averaging are standard tools for achieving mean robustness and consistency, they are not inherently compatible. Combining them can compromise optimality.…

基于 Metropolis 调整狄利克雷序列的离散数据非参数预测推断AI 解读

This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disadvantages of traditional mixture models, we develop a novel Bayesian predictive approach. In particular, our Metropolis-adjusted Dirichlet (mad) sequence model characterizes the predictive measure as a mixture of a base measure and Metropolis-Hastings kernels centered on previous data points. The…

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…

整群随机对照试验中平均处理效应估计量的精确方差AI 解读

Summary In cluster randomized controlled trials (CRCTs) with finite populations, the exact design-based variance of the Horvitz–Thompson (HT) estimator of the average treatment effect (ATE) depends on the joint distribution of unobserved, cluster aggregates of potential outcomes and is therefore not point-identified. We study a two-stage sampling design in which clusters are sampled at random, units are then sampled within selected clusters, and treatment is assigned at the…

近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…

动态网络形成的非参数推断：对“动态网络形成中的同质性与传递性”的评论AI 解读

1 IntroductionDano et al. (2026) develop a novel conditional logit approach to identify the structural parameters of a model of dynamic network formation. They propose a conditional maximum likelih...

第一类与第二类错误约束下的最优水印生成AI 解读

Abstract Watermarking has recently emerged as a crucial tool for protecting the intellectual property of generative models and for distinguishing AI-generated content from human-generated data. Despite its practical success, most existing watermarking schemes are empirically driven and lack a theoretical understanding of the fundamental trade-off between detection power and generation fidelity. To address this gap, we formulate watermarking as a statistical hypothesis…

弱因子增强回归模型的偏差校正AI 解读

In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, $0<α_{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the…

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真实模型包含在候选集中时最小二乘模型平均的渐近最优性AI 解读

Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or on the assumption that there is no true model with fixed dimensions in the candidate set. The focus of this article is to overcome these difficulties by studying a class of least squares model averaging methods with nested candidate models, which includes Mallows model averaging (MMA) of Hansen (2007, Econometrica 75,…

持久时间序列中随机周期的估计AI 解读

A number of economic, financial, and climatic time series exhibit persistent cycles which are characterized by time-dependence patterns and peaks in the spectrum. In this article, we introduce a class of semiparametric cyclical–memory processes which enable the modeling of random cyclical patterns in stationary and non-stationary time series. We develop a theoretical background and asymptotic estimation theory for the frequency of a cycle represented by the location of a…

动态面板处理效应的双重／去偏机器学习AI 解读

We introduce a novel framework for causal inference in dynamic panel data that extends the cross-sectional Double/Debiased Machine Learning (DML) approach. The method rests on a partially linear dynamic panel model with two-way fixed effects for potential outcomes and readily accommodates binary, multi-valued, or continuous treatments. To translate potential outcome models into an estimable form, we introduce dynamic conditional independence assumptions. By integrating…

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…

非参数趋势曲线的多尺度比较AI 解读

We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in examining the trending behaviour of multiple time series. Among other things, they would like to know which time series have different trends and in which time intervals the differences occur. We design a multiscale test to formally approach these questions. Specifically, we develop a test which allows to make rigorous confidence…