每日简报

基于凸M估计的似然比型检验的自助法AI 解读

Abstract We consider likelihood-ratio-type statistics for testing restrictions on parameters built from a convex M-estimation criterion. In general, these statistics are not asymptotically pivotal. We propose a general simple nonparametric bootstrap procedure that yields asymptotically valid critical and p-values. The method modifies the bootstrap objective function to mimic what happens under the null hypothesis. A Monte-Carlo study illustrates that our method controls…

心理测量结局缺失时高维预测变量的贝叶斯选择AI 解读

High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not accommodate hierarchical predictor structures and are primarily designed for univariate outcomes. We propose SHIM, a Bayesian framework for structured variable selection that combines hierarchical horseshoe shrinkage with a Bayesian treatment of missing outcomes. The framework jointly accommodates…

比例高维线性模型中的最优线性泛函估计AI 解读

We study estimation of a linear functional in random-design linear models when $γ=p/n \in (1, \infty)$ under the dense regime where the design covariance is unknown, and neither the regression vector nor the precision matrix is assumed sparse. Suppose the spectrum of the design covariance belongs to $[\ell,L]$, we establish the minimax mean squared rate for the linear functional has the order $n^{-α(κ,γ)}$, where the exponent $α(κ,γ)$ has an explicit elliptic-integral…

线性分位数面板模型的识别AI 解读

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

面向政策选择的实验设计AI 解读

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The…

协变量自适应随机化中的离散化：收益与损失AI 解读

Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment…

未知阻尼参数下二维线性抛物型随机偏微分方程的小噪声渐近分析AI 解读

We study parametric estimation for second order linear parabolic stochastic partial differential equations in two space dimensions with a small volatility parameter driven by a $Q$-Wiener process with an unknown damping parameter using high frequency spatio-temporal data. We first provide an estimator for the damping parameter of the $Q$-Wiener process utilizing realized quadratic variations based on spatial and temporal increments. We next propose minimum contrast…

生存结局的隐私保护因果元中介分析AI 解读

Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information…

缺失数据下广义潜因子模型的初始化、隐式正则化与推断AI 解读

Generalized latent factor models provide a flexible framework for analyzing high-dimensional non-Gaussian data, but principled estimation and uncertainty quantification under missingness remain substantially less developed. We develop a theory that connects a computationally tractable nonconvex procedure directly to statistical inference for nonlinear latent factor models with exponential-family links and partially observed entries. Our procedure combines a link-aware…

生成式营销组合建模：连接生成引擎优化、营销与业务影响AI 解读

Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities.…

拟最大似然断点估计的锐信噪比阈值AI 解读

We establish a sharp pathwise signal-to-noise criterion for quasi-maximum-likelihood (QML) estimation of a dominant breakpoint in the second-moment structure of a multivariate time series: the QML estimator is consistent whenever the between-regime contrast exceeds the within-regime fluctuation by an explicit factor, and below this threshold global recovery can fail. Two innovations drive the result. First, the framework is pathwise: no stochastic model is imposed on the…

同时聚类正交化AI 解读

Identification in vector autoregressions involves two distinct choices: how extensively to orthogonalize shocks and whether orthogonality is imposed sequentially or simultaneously. Generalized identification imposes no orthogonality; Sims (1980} imposes full orthogonality sequentially; Francis et al. (2026) impose full orthogonality simultaneously; and Buchwalter et al. (2026a) provide clustered partial orthogonalization sequentially. We fill the remaining case by developing…

超越平均处理效应的最优协变量调整：已处理人群与重叠加权目标AI 解读

Graphical causal inference supplies a complete theory of efficient covariate adjustment for the average treatment effect: one adjustment set, computable from the graph, is optimal under every compatible distribution. We show that this is a property of the average treatment effect's inverse-prevalence weights, not of causal estimands in general. For the average treatment effect on the treated we index the efficiency bound by the adjustment set and derive exact identities for…

利用市场信息网络建模并评估金融极端事件预测AI 解读

Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution. To address this, we introduce a time-dependent network Hüsler-Reiss model in which market-informed adjacency matrices determine how strongly observations contribute to the estimation. We propose binary and weighted specifications,…

熵正则化逆强化学习的统计分析AI 解读

Inverse reinforcement learning aims to infer the reward function that explains expert behavior observed through trajectories of state--action pairs. A long-standing difficulty in classical IRL is the non-uniqueness of the recovered reward: many reward functions can induce the same optimal policy, rendering the inverse problem ill-posed. In this paper, we develop a statistical framework for Inverse Entropy-regularized Reinforcement Learning that resolves this ambiguity by…

高效稀疏典型相关分析AI 解读

In high-dimensional settings, Canonical Correlation Analysis (CCA) often fails, and existing sparse methods force an untenable choice between computational speed and statistical rigor. This work introduces a fast and provably consistent sparse CCA algorithm (ECCAR) that resolves this trade-off. We formulate CCA as a high-dimensional reduced-rank regression problem, which allows us to derive consistent estimators with high-probability error bounds without relying on…

稀疏广义线性模型的 Bernstein–von Mises 定理AI 解读

We establish an oracle Bernstein-von Mises theorem for high-dimensional sparse generalized linear models under an outcome-independent spike-and-slab prior. The posterior consistently recovers the true active set and converges in total variation to the Gaussian law that would arise if this set were known in advance, even when the active dimension grows. The result holds for both ordinary and fractional posteriors, the latter with the expected variance inflation under…

多处理、多结果与未测混杂下的部分识别AI 解读

Estimating the health effects of multiple air pollutants is a crucial problem in public health, but one that is difficult due to unmeasured confounding bias. Motivated by this issue, we develop a framework for partial identification of causal effects in the presence of unmeasured confounding in settings with multiple treatments and multiple outcomes. Under a factor confounding assumption, we show that joint partial identification regions for multiple estimands can be more…

异质性处理效应下的三重差分设计AI 解读

Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows another subgroup of the population -- potentially less impacted by the treatment -- to serve as a comparison for the subgroup of interest. While literature on difference-in-differences has discussed heterogeneity in treatment effects between treated and control groups or over time, relatively little…

用于波动率模型的稳健化高斯拟贝叶斯信息准则AI 解读

We develop a theoretical foundation for robust model comparison in a class of non-ergodic continuous volatility regression models contaminated by finite-activity jumps. Using the density-power weighting and the Hölder(-inequality)-based normalization of the conventional Gaussian quasi-likelihood function, we propose two Schwarz-type statistics and also establish their model selection consistency with respect to the minimal true parametric volatility coefficient. Numerical…

模型误设下的广义协方差估计量AI 解读

This paper investigates the properties of the Generalized Covariance (GCov) estimator under misspecification with application to processes with local explosive patterns, such as causal-noncausal processes. We show that GCov is consistent and has an asymptotically Normal distribution under misspecification. Then, we construct GCov-based Wald-type and score-type tests to test one specification against the other, all of which follow a $χ^2$ distribution. We validate the…

FactorFlow：结合大语言模型解释的因子分析可视化工作空间AI 解读

In exploratory factor analysis (EFA), one aims to describe latent variables by constructing a factor model based on the relationships among manifest variables. For a model to be useful, it is not enough that it is grounded on data; it must also be meaningful. Hence, in practice, one attempts to interpret different factor models to identify a meaningful, coherent, and theoretically defensible latent structure. Doing so, however, is not straightforward, as it is subjective and…

利用补充样本检验参数模型中的可观测变量选择假设AI 解读

In panels with sample selection (that may occur due to attrition, nonresponse, etc.), the assumption of selection on observables (missing at random, MAR) is commonly imposed despite often being implausible. However, this assumption becomes testable when a refreshment sample is available. We develop a statistical test of MAR based on a distance between two estimated distributions: one obtained using the standard inverse probability weighting (IPW) that is valid under MAR and…

识别设计AI 解读

This paper introduces a model of identification design in which the decision maker observes the population distribution of signals generated by an information structure and ranks actions by their worst-case payoff over the admissible state distributions consistent with those signals. The environment is manipulable if every action is implementable under every admissible distribution of the state variable, and strongly so if this is achievable via almost fully informative…