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不完全依从的协变量自适应随机化中，利用回归调整提高估计效率AI 解读

Abstract We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified…

从诊断性检验看论文发表过程中的选择压力AI 解读

Abstract Economics papers increasingly report balance, pretrend, placebo, and other “sniff tests,” rejection of which is bad news for authors, undermining the credibility of their main results. We derive nonparametric bounds on the latent proportion of significant sniff tests removed by the publication process (whether by p-hacking or relegation to the file drawer) and the proportion whose significance was due to true misspecification, not bad luck. Using a hand-collected…

广义非参数模型中深度神经网络估计量的统计推断AI 解读

While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between estimation errors and inputs to establish the error bound, a condition often violated in…

单调连续处理效应曲线的双重稳健逐点置信区间AI 解读

We study nonparametric inference for the causal dose–response curve when the treatment variable is continuous rather than discrete. We develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, based on inverting a likelihood ratio-type test. Monotonicity of the treatment effect curve is often a very natural assumption, and this assumption removes the need to choose a smoothing or tuning…

结果延迟下的协变量调整响应自适应设计AI 解读

Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These designs dynamically adjust treatment allocations during interim analyses based on participant responses and covariates collected during the experiment. However, delayed responses can significantly compromise the effectiveness of CARA designs, as they hinder timely adjustments to treatment assignments…

二元结局下的个体风险量化AI 解读

Abstract Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article…

利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

高维混杂下条件平均处理效应的非参数估计AI 解读

SUMMARY This study considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties such as consistency,…

个体数据与汇总统计量的半参数有效融合AI 解读

Suppose we have individual data from an internal study and various summary statistics from relevant external studies. External summary statistics have the potential to improve statistical inference for the internal population; however, it may lead to efficiency loss or bias if not used properly. We study the fusion of individual data and summary statistics in a semiparametric framework to investigate the efficient use of external summary statistics. Under a weak…

跨异质地点的因果效应迁移估计AI 解读

We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites”/“contexts”). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental” sites in which an intervention has already been implemented to a new “target” site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the…

利用空间建模处理协变量测量误差AI 解读

We propose a new estimation methodology to address the presence of covariate measurement error by exploiting the availability of spatial data.The approach uses neighboring observations as repeated measurements, after suitably controlling for the random distance between the observations in a way that allows the use of operator diagonalization methods to establish identification.The method is applicable to general nonlinear models with potentially nonclassical errors and does…

一般条件下基于局部投影的推断AI 解读

Abstract This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under…

序贯变点定位的检测后推断AI 解读

Abstract This article addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting inference following a detected change. We develop a very general framework to construct confidence sets for the unknown changepoint using only the data observed up to a data-dependent stopping time at which an arbitrary sequential detection algorithm declares a change. Our framework is nonparametric, making no assumption on the composite postchange…

因果 K 均值聚类AI 解读

Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis. We address this problem by proposing Causal k-Means Clustering, a framework that uses k-means clustering ideas to recover unknown subgroup structure from individual level…

深度学习中分支与嵌套超参数的贝叶斯优化AI 解读

Hyperparameter optimization plays a crucial role in the success of neural networks as hyperparameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesian optimization based on Gaussian process is widely used. Despite numerous applications in deep learning, the existing methods rely on a convenient but restrictive assumption that the tuning parameters are independent of each other. However, tuning parameters with…

微分方程建模中的非参数先验学习AI 解读

This article addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior distributions and derive a generalization error estimate, which accommodates data-dependent priors by extending the…

多元混合数据的最大二项似然方法AI 解读

Multivariate mixture data analysis presents numerous challenges and constitutes a vital area of interest in the fields of statistics and data science. Research into multivariate mixture structures holds relevance across diverse application domains and plays a pivotal role in the advancement of artificial intelligence and machine learning. In this article, we focus on nonparametric estimation techniques for multivariate mixture data. Specifically, we assume a known number of…

广义点过程可加模型AI 解读

Abstract In this article, we propose a generalized point process additive model with a scalar response and high-dimensional point process predictors. Our proposal is built upon four key components: a realization of a point process as a random counting measure, a generalized point process regression framework, a new kernel function for random measure through kernel embedding, and a suite of low-dimensional structures including the additive model, reduced basis representation,…

Kundu与Schmidt-Hieber对《流形假说的统计探索》的讨论AI 解读

The authors have done an admirable job by combining new methodology, meaningful statistical theory and applications all within one article. The work is both deep and accessible. As a first discussion point, let us relate the proposed latent metric model (LMM) to functional PCA. This comparison can serve as basis to borrow strength and to advance the understanding of both approaches or even to unify them. The latent metric model assumes that we observe where Z1,…,Zn∼μ are…

朱福康与郭祥宇对《流形假说的统计探索》的讨论AI 解读

We congratulate the authors on their introducing theoretical explanations and applications of the manifold hypothesis in statistics based on the latent metric space model. Using the continuity of the mean correlation kernel in the latent metric space model, the authors employ a feature map to connect the metric space of latent variables to a Euclidean space. This links the geometric and statistical properties of the model. Furthermore, the smoothness of the mean correlation…

乘积形式特征分配模型的贝叶斯分析AI 解读

Abstract Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the popular Indian buffet process. This class plays a prominent role among existing priors, and it shares structural characteristics with Gibbs-type priors in the species sampling framework. We develop a general theory for the entire…

流形上热核高斯过程的可扩展贝叶斯推断AI 解读

Abstract We establish a scalable manifold learning method and theory, motivated by the problem of estimating functional magnetic resonance imaging activation manifolds in the Human Connectome Project. Our primary contribution is the development of an efficient estimation technique for heat kernel Gaussian processes in the exponential family model. This approach handles large sample sizes n, preserves the intrinsic geometry of data, and significantly reduces computational…

用于导数建模的最优代入式高斯过程AI 解读

Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy…

人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…