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回归中自适应稀疏性与收缩的贝叶斯方法AI 解读

Bayesian and frequentist regularization techniques are commonly employed for estimating regression models from data. However, most existing methods require practitioners to pre-specify whether the target model is dense or sparse. A recent proposal, the GLP prior distribution, addresses this limitation by bridging the gap between sparse and dense model regimes in a data-driven manner. Despite its flexibility, the GLP prior incurs high computational costs due to matrix…

单一积分型尾部形状参数下极端 VaR 与 ES 的联合动态AI 解读

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, that is, integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs…

动态 CoVaR 建模与估计AI 解读

The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank’s shares) being in distress. We introduce a two-step M-estimator for the…

利用混合整数规划检测时间序列变点AI 解读

We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the l0-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a…

利用辅助数据界定遗漏变量偏差：邻里效应估计应用AI 解读

This paper develops a method for estimating a superset that contains the linear projection coefficients of an outcome variable y onto a full set of covariates (x,z,w), when the available data are split across two independent sources. One dataset identifies the joint distribution of y and a subset of covariates (z,w), while the other identifies the joint distribution of (x,z). This framework is motivated by applications where administrative data lacks information on…

跨时间平滑方差：自适应随机波动率AI 解读

We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We…

区分宏观数据因子模型中的结构断点AI 解读

We develop a projection-based decomposition to disentangle structural breaks in the factor variance and factor loadings. Our approach yields test statistics that can be compared against standard distributions commonly used in the structural break literature. Because standard methods for estimating factor models in macroeconomics normalize the factor variance, they do not distinguish between breaks of the factor variance and factor loadings. Applying our procedure to U.S.…

从惩罚到过参数化：高维线性变量误差模型的隐式正则化AI 解读

Regularization methods are crucial for the analysis of high-dimensional data, with most methods adding a penalty term to the loss function explicitly. In this paper, we introduce an implicit regularization technique through over-parameterization and propose a calibrated penalty-free (CPF) estimation method for high-dimensional linear errors-in-variables models. This method calibrates the bias caused by measurement errors while avoiding the bias introduced by penalty terms.…

线性预测回归中条件期望分位数型风险价值的新估计量AI 解读

Since it is the only elicitable law-invariant coherent risk measure, the expectile-based value-at-risk (EVaR) is a recently recommended risk measure in financial risk management. This paper considers the large sample statistical inference problem of conditional EVaR under a linear predictive regression model. Based on the least-squares residuals, we propose a novel least-squares residual estimator for the conditional EVaR of a linear predictive regression. The asymptotic…

律师的价值：以附带来源规则变更作为无效工具变量AI 解读

ABSTRACT The value of lawyers to their clients is notoriously difficult to estimate because of endogeneity. We utilize modifications to the collateral source (CS) rule that require reducing trial awards by the amount of payments from first‐party insurance as an instrument for hiring a lawyer. The problem with our instrument is that modifications to the CS rule have a direct effect on recovery and, hence, violate the exclusion restriction required for a valid instrument. We…

异质性面板数据模型的估计及其在项目评估中的应用AI 解读

This article studies panel data models with two-dimensional unobserved slope heterogeneity and interactive fixed effects. We propose a two-step approach to estimate the parameters in the model. We apply the proposed model to estimate heterogeneous treatment effects at both individual and aggregate levels. Monte Carlo simulations show that the proposed estimators perform remarkably well in finite samples in comparison with some existing methods such as the synthetic control…

半参数广义帕累托模型中的尾部期望分位数估计AI 解读

Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectile is beyond the range of the data. This problem has been studied only fairly recently, using solely the Weissman extrapolation method. The competing…

交叉设计的因果效应、有效估计与稳健推断AI 解读

Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However, standard analyses of crossover designs…

基于预测生成树方法的贝叶斯空间区域划分一致估计AI 解读

Bayesian model-based spatial clustering methods are widely used for their flexibility in estimating latent clusters with an unknown number of clusters while accounting for spatial proximity. Many existing methods are designed for clustering finite spatial units, limiting their ability to make predictions or may impose restrictive geometric constraints on the shapes of subregions. Furthermore, the posterior clustering consistency theory of spatial clustering models remains…

空间混杂下高斯随机场回归斜率的填充渐近一致可估性AI 解读

The problem of estimating the slope parameter in regression between two spatial processes under confounding by an unmeasured spatial process has received widespread attention in the recent statistical literature. Yet, a fundamental question remains unresolved: when is this slope consistently estimable under spatial confounding, with existing insights being largely empirical or estimator-specific. In this manuscript, we characterize conditions for consistent estimability of…

流形上的粒度平滑AI 解读

Given a random sample from a density function supported on a manifold M, a new method for the estimating highest density regions of the underlying population is introduced. The new proposal is based on the empirical version of the opening operator from mathematical morphology combined with a preliminary estimator of the density function. This results in an estimator that is easy-to-compute since it simply consists of a list of centers and a radius r that are adequately…

二维 Navier–Stokes 方程的数据同化：后验测度的最优高斯渐近性质AI 解读

A functional Bernstein–von Mises theorem is proved for posterior measures arising in a data assimilation problem with the two-dimensional Navier–Stokes equation where a Gaussian process prior is assigned to the initial condition of the system. The posterior measure, which provides the update in the space of all trajectories arising from a discrete sample of the (deterministic) dynamics, is shown to be approximated by a Gaussian random vector field obtained as the solution to…

数据增强的高斯与非高斯普适性AI 解读

We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical prediction risk. It can act as a regularizer, but fails to do…

半收缩漂移分数布朗运动驱动随机微分方程的平稳密度快速估计AI 解读

We study the estimation of the invariant density of additive fractional stochastic differential equations with Hurst parameter H∈(0,1). We first focus on continuous observations and develop a kernel-based estimator achieving faster convergence rates than previously available. This result stems from a martingale decomposition combined with new bounds on the (conditional) convergence in total variation to equilibrium of fractional SDEs. For H<1/2, we further refine the rates…

从杀死扩散过程推断扩散率AI 解读

We consider diffusion of independent molecules in an insulated Euclidean domain with unknown diffusivity parameter. At a random time and position, the molecules may bind and stop diffusing in dependence of a given “binding potential.” The binding process can be modeled by an additive random functional corresponding to the canonical construction of a “killed” diffusion Markov process. We study the problem of conducting inference on the infinite-dimensional diffusion parameter…

异质性调整下分布式精度矩阵的最优整合估计AI 解读

Distributed learning offers a practical solution for the integrative analysis of multisource datasets, especially under privacy or communication constraints. However, addressing prospective distributional heterogeneity and ensuring communication efficiency pose significant challenges on distributed statistical analysis. In this article we focus on integrative estimation of distributed heterogeneous precision matrices, a crucial task related to joint precision matrix…

有向网络互惠效应的最优估计与检验AI 解读

The $p_1$ model plays a fundamental role in modeling directed networks, where the reciprocal effect parameter $ρ$ is of special interest in practice. However, due to nonlinear factors in this model, how to estimate $ρ$ efficiently is a long-standing open problem. We tackle the problem by the cycle count approach. The challenge is, due to the nonlinear factors in the model, for any given type of generalized cycles, the expected count is a complicated function of many…

似然模型中附带参数问题的 Neyman 正交化方法AI 解读

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate. It may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading…

大冲击宏观模型的快速高效估计AI 解读

We propose a new approach for efficiently estimating and analyzing macroeconomic models subject to large shocks. We apply the methodology to a two-sector model that captures the heterogeneous exposure of different sectors to the COVID-19 pandemic. We solve the model nonlinearly and propose a new nonlinear, non-Gaussian filter designed to handle large shocks and identify their source and time location. Monte Carlo experiments show that the estimation and identification of…