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两步分位数回归中 CoVaR 推断的渐近理论AI 解读

Measuring systemic risk has been an important topic in recent research on risk management. Adrian and Brunnermeier (2016) pioneered the CoVaR measure for systemic risk, which is defined as the conditional quantile of system loss given some state predictors and one stress event of an individual loss at a certain risk level. They proposed to estimate CoVaR by a two-quantile-regression approach, which is simple and easy to implement in practice, but without stating or…

高维协变量下局部平均处理效应的识别稳健推断AI 解读

This paper presents an inference method for the local average treatment effect (LATE) in the presence of high-dimensional covariates, regardless of the strength of identification. We propose an orthogonalized Anderson-Rubin test statistic that maintains uniformly valid asymptotic size. We provide an easy-to-implement algorithm for inferring the high-dimensional LATE by inverting our test statistic and employing the double/debiased machine learning method. Simulation results…

由因果 GARCH 波动驱动的非因果自回归过程AI 解读

This paper studies the introduction of causal conditional heteroskedasticity in noncausal autoregressive (AR) models. We demonstrate that, in this framework, large shocks to the innovation process driving the GARCH error term of a noncausal AR model result in heightened volatility immediately before and after a bubble crash. The non-coincidence of the σ-fields generated by past observations and past values of the GARCH process makes statistical inference nonstandard. In…

稀疏网络的拟合优度检验AI 解读

The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the best of our knowledge, however, none of the existing approaches are applicable for sparse networks in which the connection probability of any two communities is of order n−1logn, and the number of communities is divergent. To fill this gap, we propose a novel goodness-of-fit test for the stochastic…

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…

含固定效应的门槛空间面板回归AI 解读

We introduce general estimation and inference methods for threshold spatial panel regression with two-way fixed effects in a diminishing-threshold-effects framework. A valid objective function is obtained through a simple adjustment on the concentrated quasi loglikelihood with fixed effects being concentrated out, which leads to a consistent estimation of all common parameters. We show that the estimation of threshold parameter has a negligible effect on the asymptotic…

矩不等式模型推断的实用指南AI 解读

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments…

Lévy过程的快速无似然参数估计AI 解读

ABSTRACT Lévy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high‐frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose a fast and accurate method for Lévy parameter estimation using the neural Bayes estimation (NBE) framework—a simulation‐based, likelihood‐free approach that leverages…

倾向得分的平滑设定检验AI 解读

ABSTRACT This paper proposes a simple smooth specification test for the propensity score in the spirit of Neyman (1937). We derive a particular restriction on the propensity score and construct the specification test based on it. Using the density‐weighting method, we transform the original specification testing problem into a test of the joint significance of the generalized Fourier coefficients. We propose a novel projection method to eliminate the parameter estimation…

干扰结构设定的条件随机化检验AI 解读

ABSTRACT This study proposes specification tests for interference structure in causal inference with spillovers. We focus on experimental settings in which the treatment assignment mechanism is known. To test whether a given exposure mapping adequately summarizes the true interference structure, we develop conditional randomization tests by utilizing the hierarchical relationship between the null and alternative exposure mappings. When there are multiple candidate exposure…

三重稳健面板估计量AI 解读

ABSTRACT This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply Robust Panel (TROP) estimator, that combines a flexible model for the potential outcomes based on a low‐rank factor structure on top of a two‐way‐fixed effect specification, with unit weights intended to upweight units similar to the treated units and time weights intended to upweight time periods close to the treated time periods. We study the…

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…

Beta 树：具有置信陈述的多元直方图AI 解读

Multivariate histograms are difficult to construct due to the curse of dimensionality. Motivated by k-d trees in computer science, we show how to construct an efficient data-adaptive partition of Euclidean space that possesses the following two properties: With high confidence the distribution from which the data are generated is close to uniform on each rectangle of the partition; and despite the data-dependent construction we can give guaranteed finite sample simultaneous…

近似邻域干扰下的辅助变量调整AI 解读

Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-world settings where interference between units is present. Network interference, in particular, has garnered significant attention. Structural models, like the linear-in-means model, are commonly used to describe interference, but they rely on the correct specification of the model, which can be…

协方差矩阵估计中的迁移学习：最优性与自适应性AI 解读

Transfer learning, which leverages auxiliary data to improve inference in a target domain, has become a powerful tool in machine learning. In this work, we study minimax and adaptive estimation of large bandable covariance matrices under a transfer learning framework.We first establish the minimax rate of convergence under squared spectral norm loss and propose a rate-optimal estimator. Our analysis reveals phase transitions determined by the source-target disparity, the…

含治愈比例的删失数据分位数回归AI 解读

Quantile regression is emerging as a valuable statistical tool for exploring complete information on the exposure-outcome relationship. This paper investigates a novel inference procedure for quantile regression models to analyze survival data with a cure fraction. A two-component mixture cure model is used, in which the cure fraction follows a logistic model and the uncured individuals follow a linear quantile regression model. Our main interest lies in the estimation of…

单阶段与多阶段抽样调查的完整样本似然估计与推断AI 解读

In the analysis of sample surveys, accounting for informative sampling design is critical to ensure estimates of model parameters such as regression coefficients are not systematically biased. One popular solution is maximum pseudo-likelihood estimation, which incorporates weights given by functions of the selection probabilities. In this article, we propose an alternative method for analytical estimation and inference in sample surveys known as complete sample likelihood or…

非均匀高维稀疏多重超图的通用潜在嵌入建模AI 解读

Recent research has shown growing interest in modeling hypergraphs, which capture polyadic interactions among entities beyond traditional dyadic relations. However, most existing methodologies for hypergraphs face significant limitations, including their heavy reliance on uniformity restrictions for hyperlink orders and their inability to account for repeated observations of identical hyperlinks. In this work, we introduce a novel and general latent embedding approach that…

对抗性污染下的稳健回归：Welsch 估计量的理论与算法AI 解读

Convex and penalized robust regression methods often suffer from a persistent bias induced by large outliers, limiting their effectiveness in adversarial or heavy-tailed settings. In this work, we study a smooth redescending non-convex M-estimator, specifically the Welsch estimator, and show that it can eliminate this bias whenever it is statistically identifiable. We focus on high-dimensional linear regression under adversarial contamination, where a fraction of samples may…

增强边际敏感性模型及其界限AI 解读

Sensitivity analysis is important to assess the impact of unmeasured confounding in causal inference from observational studies. The marginal sensitivity model (MSM) provides a useful approach in quantifying the influence of unmeasured confounders on treatment assignment and leading to tractable sharp bounds of common causal parameters. In this paper, to tighten MSM sharp bounds, we propose the enhanced MSM (eMSM) by incorporating another sensitivity constraint which…

极值指数的高维推断AI 解读

When applying multivariate extreme value statistics to analyze tail risk in compound events defined by a multivariate random vector, one often assumes that all dimensions share the same extreme value index. While such an assumption can be tested using a Wald-type test, the performance of such a test deteriorates as the dimensionality increases.This paper introduces novel tests for comparing extreme value indices in high-dimensional settings, under both weak and general…

线性模型的随时有效推断及回归调整因果推断应用AI 解读

Linear models are foundational tools in statistics and ubiquitous across the applied sciences. However, conventional statistical tests, such as t-tests and F-tests, are only valid at fixed sample sizes, making them unsuitable for sequential settings such as online A/B testing. We develop an anytime-valid theory of inference for the linear model, introducing sequential analogues of classical tests and confidence sets that provide Type-I error control and coverage guarantees…

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,…