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枢轴量存在与否对小区域经验最佳线性预测区间覆盖率的影响AI 解读

We advance parametric bootstrap theory to construct highly efficient empirical best prediction intervals for small area means, achieving a coverage error rate of O(m−3/2), where m is the number of areas modeled by a linear mixed normal model. For a general mixed effect model with random effects from a known but nonnormal distribution (with unknown hyperparameters), we show analytically that the empirical best linear (EBL) prediction interval maintains the same coverage error…

Rasch 模型题目参数估计的随机配对极大似然方法AI 解读

The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals’ latent traits and their binary responses to assessments or questionnaires. In this article, we introduce a new likelihood-based estimator—random pairing maximum likelihood estimator (RP‐MLE) and its bootstrapped variant multiple random pairing MLE (MRP‐MLE) which faithfully estimate the item parameters in the Rasch model. The new…

复杂时间动态下单调且平滑时变函数的同时推断AI 解读

We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done utilizing the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The…

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多维聚类与序列相关时间效应下的野生自助法推断AI 解读

This article studies wild bootstrap-based inference for regression models with multiway clustering. Our proposed methods are multiway counterparts to the (one-way) wild cluster bootstrap approach introduced by Cameron, Gelbach, and Miller. We establish the validity of our methods for studentized statistics. Theoretical results are provided, accommodating arbitrary serial dependence in the common time effects—an aspect excluded by existing two-way bootstrap-based approaches.…

双重稳健的交错双重差分组别—时期条件平均处理效应一致置信带AI 解读

We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant’Anna. Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group-time conditional average treatment effect given the covariate. Focusing on the case of a continuous covariate, we propose a…

内生性下的极端分位数处理效应AI 解读

We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures good performance even in extreme tails, where data may be sparse. Simulation studies confirm the…

利用再现样本进行高维线性回归模型与系数的有限样本及大样本推断AI 解读

In this paper, we present a novel and effective inference approach to conduct both finite and large sample inference for high-dimensional linear regression models. This approach is developed under the so-called repro samples framework, in which we conduct statistical inference by creating and studying the behavior of artificial samples that are obtained by mimicking the sampling mechanism of the data. We construct confidence sets for (a) the true model corresponding to the…

正则化M估计量装袋集成的精确渐近分析AI 解读

We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous collection of M≥1 regularized M-estimators, each trained with (possibly different) subsample sizes, convex differentiable losses, and convex regularizers. We operate under the proportional asymptotics regime, where the sample size n,…

再生核希尔伯特空间中非参数随机逼近的可扩展推断AI 解读

Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, particularly well suited for large-scale and streaming data settings. In this work we propose a theoretical framework for stochastic approximation (SA) applied to nonparametric least squares in reproducing kernel Hilbert spaces (RKHS), enabling online statistical inference in nonparametric regression…

时变方差下瞬时因果关系的非参数检验AI 解读

This paper proposes a consistent nonparametric test with good sampling properties to detect instantaneous causality between vector autoregressive (VAR) variables with time-varying variances. The new test takes the form of the U -statistic, and has a limiting standard normal distribution under the null. We further show that the test is consistent against any fixed alternatives, and has nontrivial asymptotic power against a class of local alternatives with a rate slower than $T^{-1/2}$…

预测回归中位于参数空间边界的参数AI 解读

We consider bootstrap inference in predictive (or Granger-causality) regressions when the parameter of interest may lie on the boundary of the parameter space, here defined by means of a smooth inequality constraint. For instance, this situation occurs when the definition of the parameter space allows for the cases of either no predictability or sign-restricted predictability. We show that in this context constrained estimation gives rise to bootstrap statistics whose limit…

地球物理张量特征值的非参数自助法推断AI 解读

Symmetric matrices (tensors) are measured in geophysics and other disciplines, including in medical imaging, and typically their eigenvalues have valuable scientific interpretations. We design pivotal bootstrap hypothesis tests of specified eigenvalues or eigenvalue multiplicities in one-sample situations and for equal eigenvalues in k-sample situations. Our tests are more broadly applicable than existing tests by allowing very general distributions, allowing three or more…

差分隐私置换检验AI 解读

Recent years have witnessed growing concerns about the privacy of sensitive data. In response to these concerns, differential privacy has emerged as a rigorous framework for privacy protection, gaining widespread recognition in both academic and industrial domains. While substantial progress has been made in private data analysis, existing methods often suffer from impracticality or a significant loss of statistical efficiency. This article aims to alleviate these concerns…

固定效应非参数面板模型的一致推断AI 解读

This article studies uniform inference on a function $g(\cdot )$ and its functionals in a nonparametric panel data model with fixed effects. The nonparametric panel model relaxes restrictions on time-series behavior by allowing for arbitrary types of stationary or nonstationary dependence (e.g., stationary mixingale, mildly stationary, or local-to-unity process). After removing the fixed effects via transformations, a sieve estimator is proposed, accompanied by Yurinskii’s…

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.…

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,…

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,…

近似随机化检验的渐近有效性与有限样本性质AI 解读

Summary Randomization tests rely on simple data transformations and possess an appealing robustness property. In addition to being finite-sample valid if the data distribution is invariant under the transformation, these tests can be asymptotically valid under a suitable studentization of the test statistic, even if the invariance does not hold. However, practical implementation often encounters noisy data, resulting in approximate randomization tests that may not be as…

匹配估计量的自助法一致性AI 解读

Summary In a landmark paper, Abadie & Imbens (2008)showed that the naive bootstrap is inconsistent when applied to nearest neighbour matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In contrast, in this work we show that the naive bootstrap is provably consistent for the original matching…

因果发现后因果效应的选择后推断AI 解读

Summary Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data…

利用外部数据检验随机临床试验中的生物标志物交互疗效AI 解读

Summary In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effects has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation, but the study design did…

利用生成式神经网络进行双重稳健条件独立检验AI 解读

Abstract This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a…

低阶多项式检测相关随机块模型的计算转变AI 解读

Detection of correlation in a pair of random graphs is a fundamental statistical and computational problem that has been extensively studied in recent years. In this work, we consider a pair of correlated (sparse) stochastic block models S(n,λn;k,ϵ;s) that are subsampled from a common parent stochastic block model S(n,λn;k,ϵ) with k=O(1) symmetric communities, average degree λ=O(1), divergence parameter ϵ, and subsampling probability s. For the detection problem of…

流式数据下函数线性回归的可扩展推断AI 解读

Traditional static functional data analysis is facing new challenges due to streaming data, where data constantly flows in. A major challenge is that storing such an ever-increasing amount of data in memory is nearly infeasible. In addition, existing inferential tools in online learning are mainly developed for finite-dimensional problems, while inference methods for functional data are focused on the offline settings. In this paper, we focus on the online estimation of…