论文检索

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…

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…

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.…

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…

协变量漂移下外部有效性的简单稳健性度量AI 解读

This paper studies the robustness of estimated policy effects to changes in the distribution of covariates, a key determinant of the external validity of (quasi)experimental results. I propose a novel robustness metric δ ∗ which measures the smallest covariate shift needed to invalidate an empirical claim about the policy effect (e.g., AT E > 0). I estimate δ ∗ via de-biased GMM, achieving a parametric rate of convergence while accommodating machine-learning estimators of…

紧识别SVAR中基于“软”符号限制的快速后验抽样AI 解读

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on ‘soft’ sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on…

深度变换模型AI 解读

The increasing data complexity leads to the demand for more general and flexible modeling approaches. We consider a nonparametric transformation model with weak assumptions, which encompasses many popular models as special cases. A rank-based deep neural network (DNN) estimation approach is developed, with a loss function significantly different from the existing ones. It can accommodate outliers in response, which is a robustness property not broadly shared. For…

高维点过程的正则化谱估计AI 解读

Summary Advances in modern technology have enabled the simultaneous recording of neural spiking activity across large numbers of neurons, which statistically can be represented by a multivariate point process. We characterise the second order structure of this process via the spectral density matrix, a frequency domain equivalent of the covariance matrix. In the context of neuronal analysis, statistics based on the spectral density matrix can be used to infer connectivity in…

结合协变量与稀疏内在得分的实体排序AI 解读

This paper addresses the item ranking problem with associated covariates, focusing on scenarios where the preference scores can not be fully explained by covariates, and the remaining intrinsic scores are sparse. Specifically, we extend the pioneering Bradley-Terry-Luce (BTL) model by incorporating covariate information and considering sparse individual intrinsic scores. Our work introduces novel model identification conditions and examines the statistical rates of the…

超越固定截断时点：临床试验中的自适应限制平均生存时间方法AI 解读

Summary Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook…

矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate (min{p1p2,p2T,p1T})−1/2 in the average Frobenius norm, where p1, p2 and T are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate is faster than that…

预测之外的良性过拟合：普通最小二乘插值估计量AI 解读

Summary Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations. Owing to its simplicity and practical relevance, the ordinary least-squares interpolator has become a key object of study for gaining theoretical insight into this phenomenon. While the properties of ordinary least squares are well understood in classical underparameterized…

多重门槛回归的 Lasso 后推断及收益可预测性应用AI 解读

This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…

合成工具变量：从稀疏关联到稀疏因果效应AI 解读

Abstract In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are…

去中心化网络上的惩罚经验似然AI 解读

Abstract Empirical likelihood encounters serious computational challenges when applied to massive datasets or multiple data sources distributed across decentralized networks. This paper proposes a constrained empirical likelihood framework for decentralized networks, utilizing a novel penalization technique to obtain a penalized empirical log-likelihood. The resulting empirical log-likelihood ratio statistic is proved to be asymptotically standard chi-squared even for a…

本地分布式多网络社区检测的隐私保护迁移学习AI 解读

Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves community detection on a \emph{target network} by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary \emph{source networks}. Our focus is the \textit{local differential privacy} regime, in which each local data provider…

设定 LASSO 与灵活的特征型资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

用于企业生存预测的高维删失 MIDAS 逻辑回归AI 解读

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors…

预印本版本摘要 · 查看来源

时变向量自回归的模型平均AI 解读

This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We…