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集约边际与广延边际上的处理效应评估AI 解读

This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to…

高维面板数据模型的稳健估计与推断AI 解读

This paper provides the relevant literature with a complete toolkit for conducting robust estimation and inference about the parameters of interest involved in a high-dimensional panel data framework. Specifically, (1) we allow for non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, (2) we develop an estimation method for the high-dimensional long-run covariance matrix using a thresholded estimator, (3) we also allow for the number of…

资产定价因子模型检验：样本外视角AI 解读

We develop the Sample Splitting Alpha (SSA) test, a formal out-of-sample (OOS) test for evaluating whether an asset pricing factor model satisfies the zero-alpha restriction in high-dimensional settings. The test uses sample splitting to separate portfolio weight estimation from model evaluation and ridge regularization to stabilize the portfolio problem when the number of test assets is large. We show that each single split SSA statistic is asymptotically Gaussian under…

高维线性因子定价模型的 Lq 范数 alpha 检验AI 解读

We consider testing zero pricing errors in high-dimensional linear factor pricing models. Existing methods are mainly based on either an $L_2$ statistic, which is effective under dense alternatives, or an $L_\infty$ statistic, which is powerful under very sparse alternatives. To bridge these two regimes, we develop a class of $L_q$-based tests for finite $q$, including the practically useful $L_4$ and $L_6$ cases. We show that larger $q$ leads to greater sensitivity to…

高维矩阵值时间序列的时空自回归模型AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals acrossmultiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

基于偏 Gini 协方差的重尾误差高维线性模型稳健推断AI 解读

This paper introduces a novel robust high-dimensional inference framework based on partial Gini covariance. We specifically address the practically important problem of quantifying how changes in features affect the conditional mean in high-dimensional linear regression when the random noises are heavy-tailed and possibly asymmetric. Conventional high-dimensional regression inference methods suffer from inaccurate type I errors and reduced power in heavy-tailed contexts.…

跨疾病临床治疗指标的关联：基于 SEER-Medicare 的深度神经网络分析AI 解读

In outcomes research, clinical treatment measures are routinely analyzed. Most existing studies focus on individual diseases, and increasing recent evidence suggests that clinical treatment measures of different diseases can be “correlated” (or interconnected). Existing studies on such interconnections are usually limited to a small number of diseases. In this study, for U.S. older patients with common cancers, we complement the existing literature and conduct a pan-disease…

概率预测中适当损失函数的非对称惩罚AI 解读

Abstract Accurately forecasting the probability distribution of phenomena of interest is a classic and increasingly widespread goal in statistics and decision theory. In comparison to point forecasts, probabilistic forecasts aim to provide a more complete and informative characterization of the target variable. This endeavour is only fruitful, however, if a forecast is ‘close’ to the distribution it attempts to predict. The role of a loss function, also known as a scoring…

高维异质中介分析的检验功效增强AI 解读

Mediation models are widely used in many scientific areas to understand how an exposure variable impacts an outcome variable through an intermediate variable. In this article, we develop powerful inferential methods to analyze mediation effects in high-dimensional contexts with intercorrelated potential mediators in models with linear and generalized linear outcomes. We draw attention to heterogeneous mediation, and show that existing testing approaches based on the total…

面向重尾数据的高维切片逆 Kendall 秩相关估计AI 解读

Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under a supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall’s tau Estimation (SIKE) designed for analyzing…

离散空间随机场的有向图模型混合AI 解读

Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian setting when full posterior inference is desired. As an alternative, we propose a novel statistical modeling framework for this data setting, namely a mixture of directed graphical models (MDGMs). The components of the mixture, directed graphical models, can be represented by directed acyclic…

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双重因子结构下高维函数型时间序列的协方差函数估计AI 解读

We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor structure is imposed on the observed functional processes, whereas a low-dimensional version (via series approximation) is assumed for the latent functional factors. We extend the classic principal component analysis technique for estimating a low-rank structure to the estimation of a large covariance matrix of…

高维均值向量的分布无关收缩估计AI 解读

We introduce shrinkage estimators of the sample mean vector in high dimension. Our estimators share desirable properties relative to existing methods: they are distribution-free, consider bona-fide target estimators, and use simple estimators of the shrinkage intensities independent of the precision matrix which are L2 consistent in high dimension. Unlike existing estimators that impose that the whitened data be an i.i.d. matrix, we only impose i.i.d. across sample…

非线性结构模型中边际效应分布与分位数的面板估计：垃圾食品需求应用AI 解读

In this paper, we propose a framework to estimate the distribution of marginal effects in a general class of structural models that allow for very general nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel and use differences in outcome variables of nearby subpopulations to obtain the…

具有社群网络结构的高维多变量已实现波动率预测AI 解读

We introduce the Community Network Heterogeneous Autoregressive model with Quarticity (CNHARQ), a novel framework that integrates network-based information to enhance the forecasting of multivariate realized volatilities. To address the curse of dimensionality, we propose a Correlation-Based Stochastic Block Model (CBSBM) to uncover latent community structures from the correlation network of realized volatilities of N assets. This approach reduces the number of unknown…

具有共同随机趋势的高维门限因子模型AI 解读

We study inference for threshold regression in the context of a large factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel way of constructing confidence intervals based on a randomized test. Our confidence intervals are constructed using the rates of convergence of the estimated threshold level, and no limiting distribution is required. We also develop a…

排序数据的静态与动态贝叶斯加性回归树模型AI 解读

Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as…

回归中自适应稀疏性与收缩的贝叶斯方法AI 解读

Bayesian and frequentist regularization techniques are commonly employed for estimating regression models from data. However, most existing methods require practitioners to pre-specify whether the target model is dense or sparse. A recent proposal, the GLP prior distribution, addresses this limitation by bridging the gap between sparse and dense model regimes in a data-driven manner. Despite its flexibility, the GLP prior incurs high computational costs due to matrix…

利用混合整数规划检测时间序列变点AI 解读

We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the l0-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a…

从惩罚到过参数化：高维线性变量误差模型的隐式正则化AI 解读

Regularization methods are crucial for the analysis of high-dimensional data, with most methods adding a penalty term to the loss function explicitly. In this paper, we introduce an implicit regularization technique through over-parameterization and propose a calibrated penalty-free (CPF) estimation method for high-dimensional linear errors-in-variables models. This method calibrates the bias caused by measurement errors while avoiding the bias introduced by penalty terms.…

高维噪声下的广义稳健自适应带宽多视图流形学习AI 解读

Multiview datasets are common in scientific and engineering applications, yet existing fusion methods offer limited theoretical guarantees, particularly in the presence of heterogeneous and high-dimensional noise. We propose Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM), a new kernel-based diffusion geometry framework for integrating multiple noisy data sources. The key innovation of GRAB-MDM is a view-dependent bandwidth selection strategy that…

基于张量收缩的高维张量信号对齐与匹配检验AI 解读

We consider two hypothesis testing problems for low-rank and high-dimensional tensor signals, namely the tensor signal alignment and tensor signal matching problems. These problems are challenging due to the high dimension of tensors and the lack of suitable test statistics. By exploiting a recent tensor contraction method, we propose and validate relevant test statistics using eigenvalues of a data matrix resulting from the tensor contraction. The matrix entries exhibit…

神经网络雅可比矩阵的谱渐近：收敛、普适性与相变AI 解读

We study the spectral properties of the Jacobian matrix in artificial neural networks and establish both its limiting spectral distribution and the second-order fluctuations of its spectrum. Our analysis uncovers fundamental phenomena, including universality properties with respect to the weight distribution and a phase transition in spectral behavior driven by the choice of activation function. Leveraging tools from random matrix theory, we first analyze a class of…

数据增强的高斯与非高斯普适性AI 解读

We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical prediction risk. It can act as a regularizer, but fails to do…