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流形时间序列的平稳性AI 解读

In modern interdisciplinary research, manifold time series data have been garnering more attention. A critical question in analyzing such data is “stationarity”, which reflects the underlying dynamic behavior and is crucial across various fields like cell biology, neuroscience and empirical finance. Yet, there has been an absence of a formal definition of stationarity that is tailored to manifold time series. This work bridges this gap by proposing the first definitions of…

分担负担：公平性调整的分类方法AI 解读

We investigate the fairness issue in classification, where automated decisions are made for individuals from different protected groups. In high-consequence scenarios, decision errors can disproportionately affect certain protected groups, leading to unfair outcomes. To address this issue, we propose a fairness-adjusted selective inference (FASI) framework and develop data-driven algorithms that achieve statistical parity by controlling the false selection rate (FSR) among…

高维线性回归中的残差重要性加权迁移学习AI 解读

Transfer learning is an emerging paradigm for leveraging multiple sources to improve the statistical inference on a single target. In this paper, we propose a novel approach named residual importance weighted transfer learning (RIW-TL) for high-dimensional linear models built on penalized likelihood. Compared to existing methods such as Trans-Lasso that selects sources in an (approximately) all-in-or-all-out manner, RIW-TL includes samples via importance weighting and thus…

一致有界邻域干扰下的因果推断AI 解读

Abstract Randomized experiments remain the gold standard for estimating treatment effects; however, network interference compromises the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. Although cluster-randomized designs help mitigate some bias, they struggle to accommodate complex network structures and cannot disentangle direct from indirect effects. To address these challenges, we develop a design-based…

利用随机化网估计量分位数构造准蒙特卡洛置信区间AI 解读

Recent advances in quasi-Monte Carlo integration have shown that for linearly scrambled digital net estimators, the convergence rate can be dramatically improved by taking the median rather than the mean of multiple independent replicates. In this work, we demonstrate that the quantiles of such estimators can be used to construct confidence intervals with asymptotically valid coverage for high-dimensional integrals. By analyzing the error distribution for a class of…

张量补全中的统计推断：最优不确定性量化与统计计算差距AI 解读

This paper presents a simple yet efficient method for statistical inference of tensor linear forms using incomplete and noisy observations. Under the Tucker low-rank tensor model and the missing-at-random assumption, we utilize an appropriate initial estimate along with a debiasing technique followed by a one-step power iteration to construct an asymptotically normal test statistic. This method is suitable for various statistical inference tasks, including constructing…

经验风险最小化中的梯度下降推断AI 解读

Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has limited its broader potential for statistical inference applications. This paper provides a precise, nonasymptotic joint distributional characterization of gradient descent iterates and their debiased statistics in a broad class of empirical risk minimization…

迈向个体层面数据融合的统一半参数理论AI 解读

We consider inference about a finite-dimensional parameter integrating samples from independent sources. A recently developed theory considers scenarios where sources align with subsets of the conditional distributions of a single factorization of the joint target distribution. While this theory applies in many settings, it falls short in important data fusion problems, such as two-sample instrumental variable analysis, settings that integrate data from epidemiological…

非零中位数 GARCH 型噪声下重尾 AR 模型的两步估计AI 解读

This paper develops a novel two-step estimating procedure for heavy-tailed AR models with nonzero median GARCH-type noises, allowing for time-varying volatility. We first establish the self-weighted quantile regression estimator (SQE) across all quantile levels τ∈(0,1) for the AR parameters θ0. We show that the SQE, less a bias, converges weakly to a Gaussian process at a rate of n−1/2. The bias is zero if and only if τ equals τ0, the probability that the noise is less than…

线性模型迭代算法的不确定性量化及其早停应用AI 解读

This paper investigates the iterates bˆ1,…,bˆT obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension p is comparable with the sample size n, that is, p≍n. The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). The paper proposes novel estimators for the generalization error of the iterate…

利用广义距离相关检验独立性AI 解读

We study the fundamental statistical inference concerning the testing of independence between two random vectors. Existing asymptotic theories for test statistics based on distance covariance can only apply to either low-dimensional or high-dimensional settings and require stringent distributional assumptions. In this work we develop a new unified distributional theory of the sample generalized distance covariance that works for random vectors of arbitrary dimensions under…

低秩张量的统计推断：异方差、次高斯性与应用AI 解读

In this paper, we consider inference and uncertainty quantification for low Tucker rank tensors with additive noise in the high-dimensional regime. Focusing on the output of the higher-order orthogonal iteration (HOOI) algorithm, a commonly used algorithm for tensor singular value decomposition, we establish nonasymptotic distributional theory and study how to construct confidence regions and intervals for both the estimated singular vectors and the tensor entries in the…

矩阵时间序列 CP 因子模型的识别与估计AI 解读

We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method (J. R. Stat. Soc. Ser. B. Stat. Methodol. 85 (2023) 127–148) for which the convergence rates of the associated estimators may suffer from small eigengaps as the asymptotic theory is based on some matrix perturbation analysis, the proposed new method enjoys faster convergence rates which are free from any eigengaps. It achieves…

Vecchia 高斯过程的概率与统计性质AI 解读

Gaussian Processes (GPs) are widely used to model dependencies in spatial statistics and machine learning. However, exact inference is computationally intractable for GP regression, with a time complexity of O(n3). The Vecchia approximation scales up computation by introducing sparsity into the spatial dependency structure, represented by a directed acyclic graph (DAG). Despite its practical popularity, this approach lacks rigorous theoretical foundations, and the choice of…

马尔可夫折棒过程AI 解读

Stick-breaking has a long history and is one of the most popular procedures for constructing random discrete distributions in statistics and machine learning. In particular, due to their intuitive construction and computational tractability they are ubiquitous in modern Bayesian nonparametric inference. Most widely used models, such as the Dirichlet and the Pitman–Yor processes, rely on i.i.d. or independent length variables. Here, we pursue a completely unexplored research…

自适应稳健置信区间AI 解读

This paper studies the construction of adaptive confidence intervals under Huber’s contamination model when the contamination proportion is unknown. For the robust confidence interval of a Gaussian mean, we show that the optimal length of an adaptive interval must be exponentially wider than that of a nonadaptive one. An optimal construction is achieved through simultaneous uncertainty quantification of quantiles at all levels. The results are further extended beyond the…

以史为鉴：事件前后收益横截面的统计推断AI 解读

Confounding events can cause false positives when testing the relationship between short-term returns around a quasi-experimental event and firm characteristics. We show that this risk is severe in practice — return-characteristic relationships are often statistically significant at the 1% level on over 30% of all trading days. Benchmarking a relationship against the distribution of the same relationship on pre-event days is effective at addressing the problem. We introduce…

部分识别变系数模型的子向量推断AI 解读

This article considers a general class of varying coefficient models defined by a set of moment equalities and/or inequalities, where unknown functional parameters are not necessarily point-identified. We propose an inferential procedure for a subvector of the varying parameters and establish the asymptotic validity of the resulting confidence sets uniformly over a broad family of data-generating processes. We also propose a practical specification test for a set of…

非平稳厚尾异方差噪声下中位数自回归模型的推断AI 解读

This article studies estimation and inference in the autoregressive (AR) models with unspecified and heavy-tailed heteroskedastic noises. A piece-wise locally stationary structure of the noise is constructed to capture various forms of heterogeneity, without imposing any restrictions on the tail index. The new nonstationary AR model allows for not only time-varying conditional features but also unconditional variance and tail index. This makes it appealing in practice, with…

大量弱工具变量下的条件似然比检验AI 解读

This article extends the validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003, Econometrica 71(4), 1027-–1048) to instrumental variable regression models with unknown homoskedastic error variance and many weak instruments. We argue that the conventional CLR test with estimated error variance loses exact similarity and is asymptotically invalid in this setting. We propose a modified critical value function for the likelihood ratio (LR) statistic…

有限样本多处理决策的极小极大遗憾规则札记AI 解读

We study minimax regret treatment rules under matched treatment assignment in a setup where a policymaker, informed by a sample of size N , needs to decide between T different treatments for a T ≥ 2 $T\geq 2$ upper T greater than or equals 2 . Randomized rules are allowed for. We show that the generalization of the minimax regret rule derived in Schlag (2006, ELEVEN—Tests needed for a recommendation , EUI working paper) and Stoye (2009, Journal of Econometrics 151, 70–81)…

存在非均衡行为的不完全信息离散博弈的非参数识别AI 解读

ABSTRACT This paper studies empirical discrete‐choice games with incomplete information under nonparametric specifications of both the payoff function and the distribution of private information. It relaxes the standard global equilibrium assumption —under which players follow equilibrium strategies for all realizations of the control variables—and introduces a weaker local equilibrium assumption that requires the equilibrium restriction only for some, but not all,…

缓冲储蓄行为的统计推断AI 解读

ABSTRACT There is an ongoing debate about the buffer‐stock saving hypothesis. This paper develops a formal test of the hypothesis by examining the sub‐unity of the marginal propensity to consume out of permanent income (MPCP). Our test relies on a novel estimator of the average derivative of a nonparametric consumption model, using constructed measures of latent permanent income shocks that accommodate nonparametric income distributions. Following Li and Vuong (1998), we use…

分布向量自回归：刻画宏观与金融依赖AI 解读

ABSTRACT This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its…