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连续时间马尔可夫链的非参数建模：可扩展的精确与近似梯度AI 解读

Abstract Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by 3 factors: quadratic growth in the number of rates as the CTMC state space expands, strong dependencies among rates, and incomplete information for many transitions. We introduce a new Bayesian framework that flexibly models CTMC rates by incorporating covariates through Gaussian processes. This approach improves…

随机实验中二元数据的因果推断AI 解读

Abstract Estimating treatment effects in networked settings is a central challenge in online controlled experiments, particularly on social media platforms. We investigate a scenario where the unit-level outcome of interest comprises a series of dyadic outcomes that record pairwise interactions between units, ranging from point-to-point messaging at the microscale to bilateral trade flows at the macroscale. Because the response is defined at the dyadic level, the treatment…

有界随机变量均值检验的最优 e 值：复合备择假设AI 解读

Abstract We derive explicitly the e-values with optimal (relative) growth rate in the worst case for testing the mean of a bounded random variable, thereby providing the first application of the (RE)GROW quality criteria beyond the assumption of mutually absolutely continuous hypotheses for e-values originally proposed by Grünwald et al. (2024). For both criteria, we explicitly characterise the alternatives that are most difficult to test against and show that they admit a…

马尔可夫转换状态空间模型中近似极大似然估计的渐近性质AI 解读

For Markov-switching state-space models, it is practically infeasible to compute the likelihood function through the Kalman filter because of the path dependence on regimes. A widely applied approximation algorithm uses a collapsing step to average out the most dated regime and truncates the regime path. This algorithm’s performance, however, has not been examined analytically. This study fills the gap. We characterize the parameters that affect the approximation error,…

状态变化型模型平均预测AI 解读

This paper proposes a novel state-varying model averaging prediction for varyingcoefficient models that accounts for parameter uncertainty and model misspecification. We develop a leave-h-out state-dependent forward-validation criterion to select state-varying combination weights. It is shown that the proposed averaging prediction is asymptotically optimal in the sense of achieving the lowest possible out-of-sample prediction risk in a class of model averaging estimators.…

多阈值变化平面自回归模型AI 解读

This paper proposes a novel multi-threshold change plane autoregressive model with an unknown number of regimes, where the transition between regimes is governed by multiple threshold variables. We introduce an estimation approach that combines adaptive group fused Lasso with mixed-integer linear programming to simultaneously estimate the number of regimes and model parameters. Asymptotic properties of the estimators are established, which show that the proposed threshold…

半参数有效的半监督统计推断AI 解读

Summary We consider statistical inference under a semi-supervised setting where we have access to both labelled and unlabelled observations. We ask the question: under what circumstances, and by how much, can incorporating the unlabelled data improve upon inference using the labelled data? To answer this question, we investigate semi-supervised inference through the lens of semiparametric efficiency theory. For general pathwise differentiable targets under semiparametric…

政党作为美国政治极化驱动因素：1927—2018AI 解读

The current polarization of elites in the U.S., particularly in Congress, is frequently ascribed to the emergence of cohorts of ideologically extreme legislators replacing moderate ones.Politicians, however, do not operate as isolated agents, driven solely by their preferences.They act within organized parties, whose leaders exert control over the rank-and-file, directing support for and against policies.This paper shows that the omission of party discipline as a driver of…

一类半参数多项选择模型的闭式估计量AI 解读

We consider a semiparametric multinomial choice model that allows for an arbitrary joint distribution of choice specific unobservables that are independent of explanatory variables. This model permits relatively flexible substitution patterns between choices. To minimize computational difficulties, we restrict attention on estimators of the model that can be expressed in closed form. We combine and extend various results from the existing literature to enforce economic…

POLAR：面向动态治疗方案的悲观模型型策略学习算法AI 解读

Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance,…

表观基因组全研究大规模因果中介检验的尾部似然比方法AI 解读

Large-scale mediation testing has attracted considerable attention in epigenome-wide studies, intending to determine whether the effect of an exposure on an outcome is mediated through specific DNA methylation sites. Statistical inference for the mediation effects is challenged by the complexity of testing a large number of composite null hypotheses. We propose a tail likelihood ratio (TLR) test that is robust to variations in the proportion of null cases, signal strength,…

检验阴性设计的敏感性分析AI 解读

The test-negative design has become popular for evaluating the effectiveness of post-licensure vaccines using observational data. In addition to its logistical convenience on data collection, the design is also believed to control for the differential health-care-seeking behavior between vaccinated and unvaccinated individuals, an important while often unmeasured confounder between the vaccination and infection. Hence, the design has been employed routinely to monitor…

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二元数据二次分配程序的渐近理论AI 解读

Abstract The quadratic assignment procedure (QAP) is a popular tool for analysing dyadic data in medical and social sciences. To test the association between 2 dyadic measurements represented by 2 symmetric matrices, QAP calculates the p-value by permuting the units, or equivalently, by permuting the rows and columns of 1 matrix in the same way. Its extension to the regression setting, known as the multiple regression QAP, has also gained popularity, especially in…

Bures-Wasserstein 流形上 Fréchet 回归的偏效应检验AI 解读

Abstract We propose a novel test for assessing partial effects in Fréchet regression on the Bures-Wasserstein manifold. Under the null hypothesis, we show that the statistic admits a degenerate V-statistic approximation whose limiting distribution is aweighted mixture of chi-squared random variables, with weights determined by the eigenvalues of an integral operator associated with a reproducing kernel Hilbert space (RKHS) kernel. We establish the asymptotic validity and…

用分数阶导数推导边际密度AI 解读

Abstract This paper presents a novel method for analytical derivations of marginal densities using the fractional derivatives of moment-generating functions. Although the method requires likelihood functions to take specific forms, its assumptions are otherwise modest. It only requires that the prior moment-generating functions exist, are finite, and are continuous and differentiable at certain points. We also present the probabilistic and statistical insights behind this…

条件双样本检验的一般框架AI 解读

Abstract We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two groups is essential while controlling for confounding variables. We begin by establishing a hardness result for conditional two-sample testing, demonstrating that no valid…

异步性与价格陈旧并存时的高频协方差稳健估计AI 解读

The Epps effect refers to the empirical decline in measured asset dependence as the sampling frequency increases. Existing studies have shown that market microstructure frictions, including asynchronous trading and price staleness, can contribute to this downward bias in high-frequency covariance estimation. In this study, we propose a novel estimator of high-frequency covariance that is robust to the concurrent presence of asynchronicity and price staleness. We establish…

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超越传统旋转：无偏稀疏表示学习与 oracle 推断AI 解读

Abstract Learning low-dimensional latent representations is a central topic in statistics and machine learning, and rotation methods have long been used to obtain sparse and interpretable representations. Despite nearly a century of widespread use across many fields, rigorous guarantees for valid inference for the learned representation remain lacking. In this paper, we identify a surprisingly prevalent phenomenon that suggests a reason for this gap: for a broad class of…

时变面板数据模型中的潜在组结构估计AI 解读

Abstract We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group memberships and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline sieves. We establish consistency, derive the asymptotic distributions of the penalized sieve estimator and its…

基于因子模型的时变自动对齐重叠聚类AI 解读

In many scientific fields such as genomics and neuroscience, data often exhibit complex structures where features can belong to multiple clusters simultaneously and vary over time. Traditional clustering methods such as k-means and hierarchical clustering typically assume static cluster structures and perform hard assignments, limiting their ability to capture the underlying overlapping and time-varying cluster structures. To address these challenges, we propose a novel…

流数据非平稳密度核估计的逐块动态平滑参数选择AI 解读

Abstract We consider nonparametric estimation of the nonstationary density of streaming data collected continuously over time. Those types of data are often available only for a limited time after being observed, for example due to storage or data privacy constraints. Therefore, analyzing them requires dynamic approaches that do not require repeated access to past data. A suitable iterative kernel estimator of nonstationary densities has been developed by Rutkowski (1989);…

多因子生产函数协整系统的估计与推断：GDP 与排放的联合建模AI 解读

Abstract This paper develops integrated modified ordinary and generalized least squares estimation for systems of cointegrating multivariate polynomial regressions, i. e., systems of equations that include deterministic variables, integrated processes and (cross-)products of non-negative integer powers of these variables as regressors. The stationary errors are allowed to be correlated across equations, over time and with the regressors. The necessity to consider integrated…

开放集标签偏移数据的半参数学习AI 解读

Abstract We study the open-set label shift problem, where the test data may include a novel class absent from the training data. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric…