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去中心化网络上的分布式卷积秩回归AI 解读

This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework, in which estimators are obtained by solving consensus-constrained optimization with kernel-smoothed rank loss. The developed estimation scheme relies solely on local node data and information shared by neighboring nodes, thereby achieving privacy preservation and high communication efficiency. For heterogeneous network…

Recanting-twin 路径特定效应的大小、符号与排序AI 解读

The framework of recanting twin path-specific effects has recently been propose to address the issue of intermediate confounding in causal mediation analysis, enabling the decomposition of the average treatment effect into identifiable fine-grained path-specific effects (PSEs). An open question, however, is the extent to which recanting-twin PSEs reflect the direction and relative magnitude of their corresponding natural PSEs. In this paper, we systematically characterize…

产品内生进入与退出下需求模型的识别和估计AI 解读

Firms introduce products in markets where they anticipate stronger demand, using information unobserved by researchers. This creates endogenous selection in demand estimation. In differentiated-product oligopolies, multidimensional demand unobservables and strategic entry can render the ordinary propensity score insufficient for selection correction. Existing approaches either restrict firms' information at entry or jointly estimate demand, pricing, and entry under strong…

半参数模型的快速在线推断AI 解读

This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data…

从密集网格到有效推断：非参数随机系数模型的正则化偏差处理AI 解读

This paper develops an inference procedure for average functionals of random-coefficient distributions, such as mean willingness-to-pay and average elasticities, when the distribution is estimated nonparametrically using the penalized fixed-grid estimator of Heiss, Hetzenecker, and Osterhaus (2022). We establish asymptotic normality of the corresponding penalized plug-in estimator centered at the functional evaluated at the penalized pseudo-true value and propose a…

通过二维马尔可夫链构建长记忆 GARCH 模型AI 解读

This paper proposes a GARCH-type volatility model in which level-and-slope updates of a latent power-law kernel generate state-dependent decay of past shocks within a two-dimensional Markov state. We derive a joint Foster--Lyapunov condition and establish positive Harris recurrence and uniqueness of the invariant distribution. Simulations show substantial low-frequency persistence in log-squared innovations, especially near the diagnostic stability boundary. Empirically, the…

涌现的传播复杂性：区分机制复杂性与相关异质性AI 解读

Simple and complex contagions differ mechanistically; multiple exposures act synergistically in the latter but independently in the former. Yet correlated mixtures of simple contagions may appear complex when inferring global contagion rules, a phenomenon we call "emergent complexity." We present a measure of contagion complexity and an inferential framework for estimating mixtures of nonparametric contagion rules from time-series data. Our work reframes past studies on…

基于微观数据的一致半非参数需求估计AI 解读

This paper develops a profiled sieve minimum-distance estimator for a semi-nonparametric differentiated-products demand model with micro-level choice data. Building on Berry and Haile (2024), the estimator uses within-market variation in consumer covariates to recover a flexible consumer-heterogeneity function and market-specific composite intercepts. Excluded price instruments then separate these intercepts into a flexible price-side function and structural demand shocks.…

剩余生存时间分位数的贝叶斯非参数因果推断：阿尔茨海默病应用AI 解读

In Alzheimer's disease research, a clinically important question is how much longer individuals would remain dementia-free beyond a given time under different baseline amyloid statuses. We address this question using observational data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) treating baseline amyloid status as the exposure. Estimation is challenging because amyloid status is confounded, time to dementia onset is heterogeneous and heavily right censored,…

条件矩限制的局部稳健核设定检验AI 解读

We develop kernel-based specification tests for semiparametric conditional moment models with high-dimensional nuisance parameters, extending existing conditional moment tests---which typically require asymptotically linear nuisance estimators---to accommodate modern machine-learning methods. The proposed locally robust kernel tests combine Neyman-orthogonal moments, cross-fitting, and reproducing kernel Hilbert space methods, yielding inference that is first-order…

随机时间序列回归模型中结构断点的非参数检测AI 解读

We propose a novel nonparametric test to detect structural breaks in the conditional mean and/or variance of a time series. Our method does not assume any specific parametric form for the dependence structure of the regressor, the time series model, or the distribution of the noise. This flexibility allows our algorithm to be applicable to a wide range of framework. We further apply the proposed test to accurately localize the changepoints and establish theoretical…

利用鞅后验推断反事实分布AI 解读

Causal inference is often focused on average effects, which can hide important aspects of the effect distributions. Here we consider the entire posterior effects distribution by estimating full counterfactual outcome distributions. We propose a methodology for inference on counterfactual distributions which builds upon the martingale posterior framework of Fong et al. (2023). This provides a highly flexible approach to estimating densities, distribution functions, and…

MOCA：采用单向交叉注意力与反馈切断的模块化因果推断框架AI 解读

Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting can perform well under favorable model specification but may become unstable in complex settings. Machine-learning and representation-learning methods provide greater flexibility, but joint optimization may allow outcome information to alter treatment representations and compromise…

核岭回归用于函数矩估计的精确泛化误差曲线AI 解读

Kernel ridge regression is a standard method for functional data analysis, but its exact behavior is less understood. We study tensor-product kernel ridge regression for estimating the $r$-th moment function of a random function based on noisy discrete observations. The formulation includes mean estimation, covariance estimation, and higher-order moment estimation in a single framework. Our main result gives a precise $1+o_{\mathbb{P}}(1)$ expansion for the $L^2$ error at…

情境自适应推断：统计与基础模型的统一视角AI 解读

Modern predictive systems are expected to adapt their behavior to the specific situation they are facing. A clinical model should not treat every patient the same; a retrieval-augmented model should change its answer when given different evidence; a mixture-of-experts model should route different inputs to different experts. We call this capability context-adaptive inference: before predicting, the system uses information about the current context to specialize its…

无穷小因果性AI 解读

Interventions can be varied continuously in many causal models. Differentiating a specified smooth intervention protocol produces vector fields on a statistical model, and their Lie brackets describe the noncommutativity of the corresponding local perturbations. We formulate this differential geometry of interventions on smooth statistical models and call the resulting framework infinitesimal causality (IC). Given a constant-rank distribution spanned by visible intervention…

时变网络估计中的装饰图极限AI 解读

We propose a unified nonparametric framework for modeling time-evolving networks using decorated graphons (also known as probability-graphons): symmetric functions that assign to each node pair a probability distribution over binary edge time series. This generalizes the static decorated-graphon construction to dynamic graphs while preserving node exchangeability and allowing temporal dynamics such as memory and periodicity. Models in which edges evolve independently given…

非均匀簇期相关下多干预阶梯楔形试验的参数化时变效应框架AI 解读

Stepped wedge design (SWD) trials typically assume that treatment effects are immediate following implementation, but this assumption is often unrealistic in pragmatic settings where effects evolve over exposure time. This impact is further complicated in multiple-intervention stepped wedge designs (M-SWDs), due to their complex crossover patterns. Existing work has addressed time-varying treatment effects for main effects at the analysis stage using nonparametric…

半参数推断的 V 折刀切法：方差、置信区间与同时置信带AI 解读

For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well understood for smooth parametric estimators, its theoretical properties for many modern semiparametric and machine-learning estimators remain largely unstudied. Nevertheless, bootstrap procedures are often used routinely in such settings, even when their validity is unknown and their computational…

贝叶斯分组回归的变分近似惩罚可信区域AI 解读

We develop a fast and accurate grouped penalized credible region approach for variable selection and prediction in Bayesian high-dimensional regression. Most existing Bayesian methods either are subject to high computational costs due to long Markov Chain Monte Carlo runs or yield ambiguous variable selection results due to non-sparse solution output. The penalized credible region framework yields sparse post-processed estimates that facilitates unambiguous grouped variable…

利用异方差神经网络学习双向因果交互AI 解读

Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence. This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments. Identification exploits conditional covariance diagonalization: when structural shocks have zero…

增强因果全向网络重建：RECONAI 解读

Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems. Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations. We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECON), that leverages an integral-based additive nonparametric ODE model to reconstruct regulatory networks from…

核岭回归的推断AI 解读

We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as preferences, sequences, and graphs. Despite the prevalence of these data--e.g., student preferences in school matching mechanisms--the inferential theory of KRR is not fully known. We construct valid and sharp confidence sets that shrink at nearly the minimax rate, allowing nonstandard regressors. Our bootstrap procedure uses…

Ramsey 式空间交互函数线性检验AI 解读

We propose a computationally straightforward test for the linearity of a spatial interaction function. Such functions arise commonly, either as practitioner imposed specifications or due to optimizing behaviour by agents. Our conditional heteroskedasticity robust test is nonparametric, but based on the Lagrange Multiplier principle and reminiscent of the Ramsey RESET approach. This entails estimation only under the null hypothesis, which yields an easy to estimate linear…