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天然气价格的实时预测AI 解读

ABSTRACT This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive (VAR) model that…

基于广义特征值的高维稀疏回归方向识别与极小极大估计AI 解读

In high-dimensional (HD) sparse linear regression, parameter selection and estimation are addressed using a constraint $l_0$ on the direction of the parameter vector. We begin by establishing a general result that identifies this direction through the leading generalized eigenspace of specific measurable matrices. Using this result, we propose a novel approach to the selection of the best subsets by solving an empirical generalized eigenvalue problem to estimate the…

随机化奇异值分解的扰动分析及统计应用AI 解读

Randomized singular value decomposition (RSVD) is a class of computationally efficient algorithms for computing the truncated SVD of large data matrices. Given an m×n matrix M̂, the prototypical RSVD algorithm outputs an approximation of the k leading left singular vectors of M̂ by computing the SVD of M̂(M̂⊤M̂)gG; here g≥1 is an integer and G∈Rn×k˜ is a random Gaussian sketching matrix with k˜≥k. In this paper we derive upper bounds for the l2 and l2,∞ distances between the…

估计警员武力升级倾向的条件有序刻板模型AI 解读

This article introduces a conditional ordinal stereotype model for estimating a police officer’s latent propensity to escalate to higher-severity force options during an encounter. Propensity to escalate is the likelihood of selecting a more serious force category than peer officers confronting the same circumstances, not necessarily implying that such force is excessive or contrary to policy. The associated conditional likelihood depends solely on data from the times and…

贝叶斯非参数准似然AI 解读

A recent trend in Bayesian research has been revisiting generalizations of the likelihood that enable Bayesian inference without requiring the specification of a data generating mechanism. This article focuses on a Bayesian nonparametric extension of Wedderburn’s quasi-likelihood, using Bayesian additive regression trees to model the mean function. Here, the analyst posits only a structural relationship between the mean and variance of the outcome. We show that this approach…

未观测混杂的平均强度敏感性分析AI 解读

Summary Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model has gained popularity in recent years owing to its good interpretability and mathematical properties. However, most existing models only consider a worst-case parameter that bounds the logit difference between the observed-data and full-data propensity scores, which may not fully capture the extent of unmeasured confounding.…

稀疏删失数据的函数型主成分分析AI 解读

Summary Functional principal component analysis is a key tool in the study of functional data, driving both exploratory analyses and feature construction for use in formal modelling and testing procedures. However, existing methods do not apply when functional observations are censored; for example, when the measurement instrument only supports recordings within a prespecified interval, thereby truncating values outside this range to the nearest boundary. A naïve application…

连续时变处理下边际结构模型的非参数有效估计AI 解读

Summary Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable nonparametric estimator exists for marginal structural models with multi-valued or continuous time-varying treatments. In this paper, we combine flexible, data-adaptive regression methods, including ensemble learning techniques, with recent developments in semiparametric efficiency theory for longitudinal…

在控制错误发现率的同时识别分布差异AI 解读

Summary We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the reference and the comparison groups, we aim to identify the comparison groups whose distributions differ from that of the reference group. Viewing this as a multiple-testing problem, we introduce a methodology that provides exact, distribution-free control of the false discovery rate. To do so, we introduce the concept of batch conformal p…

叠加点过程的 Palm 分布及其统计推断AI 解读

Abstract Palm distributions play a central role in the study of point processes and their associated summary statistics. In this work, we characterize the Palm distributions of the superposition of independent point processes, establishing a simple mixture representation depending on the point processes’ Palm distributions and moment measures. We explore two statistical applications enabled by our main result. First, we consider minimum contrast estimation for corrupted…

序贯 Gibbs 后验及其主成分分析应用AI 解读

Summary Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter that weights the information in the loss relative to the prior and provides control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference; however, in many situations, the inclusion of a single tuning parameter inevitably leads to poor uncertainty quantification. In particular,…

多处理与正值性违背下的因果参数比较AI 解读

Summary Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, each targeting adifferent treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption: every subject has a nonzero probability of receiving each treatment. This assumption is often implausible, especially when treatment can take many values. Causal…

动态协变量平衡：利用潜在结果局部投影估计跨期处理效应AI 解读

Abstract This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on the trajectory of all past treatments; and (iii) heterogeneity of treatment effects. Our approach…

维度与样本量成比例时逻辑回归的 Diaconis–Ylvisaker 先验惩罚似然AI 解读

Summary We characterize the behaviour of the maximum Diaconis–Ylvisaker prior penalized likelihood estimator in high-dimensional logistic regression, where the number of covariates is a fraction $ \kappa\in(0,1) $ of the number of observations $ n $, as $ n\to\infty $. We construct a rescaled estimator with zero asymptotic aggregate bias, and define adjusted $ Z $-statistics and rescaled penalized likelihood ratio statistics that exhibit the typical null asymptotic…

不完全区组设计的设计型因果推断AI 解读

Summary Researchers often turn to block randomization to increase the precision of their inference or for practical reasons, such as in multi-site trials. However, if the number of treatments under consideration is large, it may not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-population, design-based framework for natural alternatives to the complete block design that do not require reducing the…

一类参数化鞅后验的渐近理论AI 解读

Summary The martingale posterior framework replaces the elicitation of the likelihood and prior with that of a sequence of one-step-ahead predictive densities for Bayesian inference. Posterior sampling then involves the imputation of unobserved quantities and can then be carried out in an expedient and parallelizable manner using predictive resampling, without requiring Markov chain Monte Carlo. Recent work has investigated the use of plug-in parametric predictive densities,…

局部平均处理效应的随机化置信集合AI 解读

Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson–Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment…

样本与类别扰动下多类别数据的均值比估计AI 解读

Summay We consider the problem of estimating ratios of means of a multivariate outcome across covariates when the data are observed with unknown sample-specific and category-specific perturbations. Our model admits a partially identifiable estimand, and we establish full identifiability by imposing interpretable parameter constraints. To reduce bias and guarantee the existence of estimators in the presence of sparse observations, we apply an asymptotically negligible and…

黎曼流形上的测地线切片采样AI 解读

Summary We propose a theoretically justified and practically applicable slice-sampling-based Markov chain Monte Carlo method for approximate sampling from probability measures on Riemannian manifolds. The latter naturally arise as posterior distributions in Bayesian inference of matrix-valued parameters, for example belonging to either the Stiefel or the Grassmann manifold. Our method, called geodesic slice sampling, generalizes hit-and-run slice sampling on $ \mathbb{R}^{d} $…

利用代理控制结局的数据整合后推断AI 解读

Summary Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variation, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by leveraging control outcomes. Using causal interpretations, we…

基于标准化秩统计量的 Wilcoxon–Wigner 矩阵潜在结构检验AI 解读

Summary This paper considers the problem of testing for latent structure in large symmetric data matrices. The goal is to develop statistically principled methodology that is flexible in its applicability, computationally efficient and insensitive to extreme data variation, thereby overcoming limitations of existing approaches. To this end, we introduce and systematically study certain symmetric matrices, called Wilcoxon–Wigner random matrices, whose entries are normalized…

校准敏感性模型AI 解读

Summary In causal inference, sensitivity models are used to assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter (which quantifies the degree of unmeasured confounding) is often difficult to interpret. For this reason, researchers sometimes compare the sensitivity parameter to an estimate of measured confounding, a process known as calibration or benchmarking. However, calibrated estimates are not always interpreted correctly, and…

点过程与随机场的谱估计AI 解读

Summary Spatial variables can be observed in many different forms, such as regularly sampled random fields (lattice data), point processes and randomly sampled spatial processes. Joint analysis of such collections of observations is clearly desirable, but complicated by the lack of an easily implementable analysis framework. We fill this gap by providing a multitaper analysis framework using coupled discrete and continuous data tapers, combined with the discrete Fourier…

利用投影乘积进行多视图子空间学习的谱框架AI 解读

Summary Multi-view data provide complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analysing such data typically requires distinguishing between shared (joint) and unique (individual) signal subspaces from noisy, high-dimensional measurements. Despite many proposed methods, the conditions for reliably identifying joint and individual subspaces remain unclear. We rigorously quantify these conditions,…