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合理设置补丁：注意力预测中的补丁长度AI 解读

Attention-based time series forecasters commonly group consecutive observations into patches. Patch length is known to have a substantial impact on forecasting performance, yet its selection remains largely empirical. We develop a theoretical framework that explains why patch length matters for forecasting accuracy and provides a principled basis for its selection. For linear state-space models, we derive an exact factorization of the forecasting coefficient matrix that…

有界GARCH过程AI 解读

We propose a novel class of Bounded GARCH (B-GARCH) processes designed to characterise the conditional heteroskedasticity of continuous time series constrained within a compact interval. While standard GARCH specifications are tailored for R-valued data, many empirical series, such as realised correlation, unemployment rates, expenditure shares, and relative humidity, are bounded. Fitting a traditional GARCH model to such data creates a severe support mismatch, frequently…

超输入凸神经网络：形状约束学习与最优传输AI 解读

We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networks with input convex neural networks (ICNNs) to create a neural network that is always convex in the input, theoretically capable of leveraging depth, and performs reliably when trained at scale compared to ICNNs. Concretely, we prove that HyCNNs require exponentially fewer parameters than ICNNs to…

We construct moment functions that are Neyman-orthogonal to a chosen order in moment-condition models that involve finite-dimensional nuisance parameters. These moment functions reduce sensitivity to estimation error in the nuisance parameters and, as such, offer a unified and tractable route to higher-order debiasing in a wide range of econometric models. The number of additional nuisance parameters required by our construction, beyond those already present in the original…

多值治疗方案的双稳健因果推断：循环神经网络方法AI 解读

We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient outcomes. A multi-valued treatment regime assigns personalized treatments from a set of competing options based on a patient's history. Existing methods for optimal treatment regimes are largely limited to binary or continuous treatments. We extend the longitudinal targeted minimum loss-based estimator (LTMLE) to accommodate multi-valued treatments by modeling…

MSR-CP：西孟加拉邦降雨模式变点估计的改进分段回归方法AI 解读

This study presents a methodology for detecting change point in precipitation series using a smooth version of segmented regression model. We examine several existing change point detection methods and identify their limitations in capturing gradual changes commonly observed in climate data. To address these limitations, we discuss an extension of the traditional segmented regression model enabling the detection of gradual shifts in trend. The developed methodology is…

未观测的考虑集与消费者福利AI 解读

Can welfare effects of price change be identified when consumers' consideration sets are heterogeneous and unobserved? We establish that the welfare distribution is the same functional of structural choice probabilities under stable but limited consideration as under full consideration. The result permits unrestricted income-effects, preference heterogeneity, and dependence between preferences and consideration. Within this class, limited consideration admits a…

去中心化稀疏分位数回归AI 解读

Distributed statistical learning has recently attracted significant attention owing to its computational efficiency and scalability in large-scale systems. However, the non-smooth quantile loss poses substantial challenges to the development of efficient decentralized algorithms for quantile regression. Existing methods may either suffer from slow convergence or rely on smoothing-based approximations that require additional stringent assumptions, particularly in…

潜在结果联合期望的识别与界AI 解读

The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects. This paper provides novel assumptions for their identification: comonotonicity and countercomonotonicity. These assumptions offer a unified framework for their identification across discrete, continuous, and mixed outcome variables. In the absence of these assumptions, we derive…

惩罚纵向对数对比分位数回归的统一坐标下降框架AI 解读

Quantile regression characterizes heterogeneous covariate effects across the response distribution, but its extension to high-dimensional longitudinal compositional data poses substantial challenges, such as the nonsmooth check loss, the unit-sum structure of compositional covariates. Existing work is confined to Lasso penalization, and does not readily extend to nonconvex penalties. We propose CDaLCqr, a unified coordinate descent algorithm for penalized longitudinal…

Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied. We develop a direct, data-adaptive test statistic for assessing conditional independence in the graph structure of stationary Gaussian time series. We establish a finite-sample, Berry--Esseen type Gaussian approximation bound for the statistic, which applies to both short-memory and long-memory time series. The testing procedure uses a…

During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur before the primary event. The treatment can affect the primary event through multiple pathways via intermediate events. With multiple intermediate events, each is a treatment-induced confounder of transitions out of the others, so we cannot identify natural or separable effects. We instead consider stochastic interventions that assign each transition the identifiable…

面板数据的双重机器学习：识别、设定与银行监管效应AI 解读

This paper examines robustness and causal interpretation of panel estimates using double/debiased machine learning. The prototype treatment setup is the 2018 US banking regulation. Under common treatment timing, we characterize identification and nuisance restrictions and derive exact bank-level representations of specified "within" and "first-difference" scores. Simulations assess panel model transformation-induced specification bias and inference under weaker overlap.…

非参数子图回归AI 解读

Estimation and inference on a single large network are challenging due to dependence among observations. This paper studies nonparametric regression for outcomes defined on groups of $p$ agents, such as the indicator that three agents in a network form a triangle. Outcomes on groups that share an agent may be dependent, while outcomes on disjoint groups are independent. I call this problem subgraph regression; it nests dyadic regression as the special case $p=2$. Its leading…

单指数奖励的非参数赌博机：最优性与自适应性AI 解读

Contextual bandits are a central framework for sequential decision-making, with applications ranging from recommendation systems to clinical trials. While nonparametric methods can flexibly model complex reward structures, they suffer from the curse of dimensionality. We address this challenge using a single-index model, which projects high-dimensional covariates onto a one-dimensional subspace while preserving nonparametric flexibility. We first develop a nonasymptotic…

dsp：基于动态收缩过程的贝叶斯时间序列建模 R 包AI 解读

dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaussian and count-data smoothing, changepoint detection, time-varying parameter regression, and volatility estimation, with posterior predictive draws available…

半连续结果的潜在空间统计学习框架：正则化偏差与边界稳定优化AI 解读

Semicontinuous outcomes characterized by an exact zero mass and heavy-tailed continuous distributions challenge empirical risk modeling and machine learning. Compound Poisson-Gamma Tweedie processes impose a rigid mean-variance coupling that distorts zero-mass probabilities under extreme tail dispersion, flattening continuous density and reducing extreme-risk discrimination. Furthermore, heuristic post-hoc target capping introduces systematic bias while leaving loss…

Sigmoid-FTRL：面向 AIPW 估计量的基于设计的自适应 Neyman 分配AI 解读

We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators for estimating the average treatment effect in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arrives, an adaptive procedure must select both a treatment assignment probability and a pair of linear predictors to be used in the AIPW estimator. Our goal is to construct an adaptive procedure that minimizes the Neyman Regret, which is the…

含大量生成控制变量的回归AI 解读

We consider linear regression when some covariates are endogenous, a variable sufficient to control for endogeneity exists but is latent, and the practitioner can generate many proxies for the latent control. For instance, one could cheaply but imperfectly impute the latent control using different AI/ML methods, each generating a different proxy. There are several options the practitioner might consider in this scenario: including all proxies or factors computed from the…

We comment on the optimally-transported generalized method of moments (OTGMM) of Schennach and Starck (2026) and give counterexamples to Theorems 2-6. In the small-error analysis, the assumptions do not ensure consistency or asymptotic normality (Theorems 2-3). In the large-error analysis, Theorem 4 states the equivalence between the OTGMM estimator and the GMM estimator with modified moments, and Theorems 5-6 assert consistency and asymptotic normality. Through…

条件相似检验的近似极小极大遗憾AI 解读

We construct approximately minimax regret optimal tests in a scalar Gaussian instrumental variables experiment. This example shows how conditionally similar tests allow a novel approximation approach, allowing us to optimize weights over alternatives to minimize worst-case power regret. Stochastic mirror descent and averaging yield finite-iteration guarantees with exact conditional similarity. We establish bounds for exact and simulated powers and illustrate the procedure…

存在终止事件时复发事件回归模型的有效估计AI 解读

Recurrent events are common in biomedical studies, and efficient methods are needed to analyse them, particularly when a terminal event may occur. Regression modelling of the marginal mean of recurrent events provides a simply interpretable approach, but standard estimation based on inverse probability of censoring weighting may not fully exploit observed event histories in multiple events settings. We demonstrate how the popular Ghosh-Lin proportional means model can be…

已实现二次协变差与收益不对称性的非参数检验AI 解读

We propose a nonparametric test of return asymmetry based on the realized quadratic covariation between a cumulative return path and its square using high-frequency data. Our null hypothesis is that the volatility path is independent of the Brownian motion driving returns. Under this null and a martingale condition, the infill limit of the statistic is conditionally Gaussian given the volatility path, with a conditional variance determined by integrated variance.…

Uncertainty quantification for individual treatment effects (ITEs) is a daunting challenge in causal inference. Motivated by recent advances in conformal prediction, several works aim to construct distribution-free prediction sets for ITEs with desired coverage under standard assumptions such as strong ignorability and overlap. In this paper, we show that such goals are fundamentally unattainable. Specifically, we establish finite-sample and asymptotic impossibility results…