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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…

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…

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…

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…

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...

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…

整合预训练语言模型知识的泊松过程主题模型AI 解读

Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advances in large language models (LLMs) offer contextualized word embeddings that capture deeper semantic meaning and relationships. We aim to leverage such embeddings to improve topic modeling.We use a pre-trained LLM to convert each document into a sequence of word embeddings. This sequence is then modeled as a Poisson point process, with its intensity…

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…

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…

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…

部分时期回归量完全缺失的非参数时变面板模型识别与估计AI 解读

This article considers a nonparametric time-varying panel data model in which the primary regressor of interest is completely missing in some periods. Under standard assumptions, identification results are established for the unknown functions and the distributions of the missing regressor. Specifically, one period of completely observed data suffices to identify the random effects model, whereas two periods are required for the fixed effects model. Following identification,…

重尾与污染下基于核的自适应Huber均值回归AI 解读

We consider robust conditional mean learning in the mathematical framework of reproducing kernel Hilbert spaces, assuming the errors can be heavy-tailed and at the same time some responses are subject to arbitrary contamination. Simultaneous robustness to both heavy tails and contamination are of interest. When there is no contamination, we establish rates when the (1+θ)-moment ( θ>0) of the error exists, which matches the minimax rate for the least squares regression as…

用于有序回归的延续比Logit结构化混合模型AI 解读

We develop a nonparametric Bayesian modeling approach to ordinal regression based on priors placed directly on the discrete distribution of the ordinal responses. The prior probability models are built from a structured mixture of multinomial distributions. We leverage the continuation-ratio logits representation to formulate the mixture kernel, with mixture weights defined through the logit stick-breaking process that incorporates the covariates through a linear function.…

观察性研究中因果推断的样本量与检验力计算AI 解读

This paper investigates the theoretical foundation and develops analytical formulas for sample size and power calculations for causal inference with observational data. By analyzing the variance of an inverse probability weighting estimator of the average treatment effect, we decompose the power calculation into three components: propensity score distribution, potential outcome distribution and their correlation. We show that to determine the minimal sample size of an…

高阶图极限函数理论：波动、退化与推断AI 解读

Exchangeable random graphs, which include some of the most widely studied network models, have emerged as the mainstay of statistical network analysis in recent years. Graphons, which are the central objects in graph limit theory, provide a natural way to sample exchangeable random graphs. It is well known that network moments (motif/subgraph counts) identify a graphon (up to an isomorphism); hence, understanding the sampling distribution of subgraph counts in random graphs…