论文检索

非参数二元选择模型的核化估计方法AI 解读

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional…

文本词项选择与分析：频率学派和贝叶斯策略及解释AI 解读

Economic analysis based on text data has expanded rapidly, yet the ultra-high dimensionality of text data presents substantial challenges for term selection and estimation. We propose the Information-Adaptive Lasso and Information-Adaptive Spike-and-Slab Lasso as novel frequentist and Bayesian approaches to address these challenges. A key theoretical contribution of this study is the establishment of the rate of convergence in term-selection consistency, which we show to be…

疫情后大型贝叶斯 VAR 的贝叶斯模型比较AI 解读

There is an increasing interest in applying variational Bayes techniques to estimating large Bayesian vector autoregressions (VARs) with stochastic volatility. However, less attention has been paid to the development of appropriate tools for comparing these high-dimensional models, especially among those designed to address COVID-19 outliers. This paper develops a marginal likelihood estimator that combines importance sampling and variational approximation for comparing…

经济增长建模的贝叶斯方法：变量选择与截面依赖AI 解读

This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…

大型贝叶斯矩阵自回归模型AI 解读

High-dimensional matrix-valued time-series are increasingly common in economics and finance. Prominent examples include large cross-region panels and dynamic economic networks. As the dimensions of the matrix grow, conventional approaches based on vector autoregressions—implemented by vectoring the matrix-valued data—become computationally infeasible. We introduce a class of large Bayesian matrix autoregressions (BMARs) that can accommodate time-varying volatility,…

玻璃盒机器学习与公司债券收益AI 解读

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics data set. We achieve state-of-the-art performance while maintaining an interpretable model structure, overcoming the accuracy–interpretability trade-off. The estimation uncovers nonlinear relationships and economically meaningful interactions in bond…

Edgeworth 隐私核算器：差分隐私组合的解析方法AI 解读

In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to efficiently compute the overall privacy loss under composition. This paper introduces the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees for private algorithms. Leveraging the f-differential privacy framework (Dong et al., 2022), the Edgeworth Accountant…

高维局部依赖数据的广义隶属度估计AI 解读

This work focuses on the mixed membership models for multivariate categorical data widely used for analyzing survey responses and population genetics data. These grade of membership (GoM) models offer rich modeling power but present significant estimation challenges for high-dimensional polytomous data. Popular existing approaches, such as Bayesian MCMC inference, are not scalable and lack theoretical guarantees in high-dimensional settings. To address this, we first observe…

估计分位数最优处理规则的序贯分类学习AI 解读

Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients’ outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of…

用于域泛化的域特定非参数回归AI 解读

We propose a domain-specific regression approach for domain generalization, taking into account possible heterogeneity among the datasets from different sources. In the proposed model, the domain-specific features are characterized through linear functionals of the marginal source distributions. The predictors are combined with the domain-specific linear functionals as inputs in the model. Using the source data sets, we estimate the domain-index function and the regression…

从树集成中提取可解释模型：计算与统计视角AI 解读

Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key…

存在隐藏混杂时过参数化模型的在线推断AI 解读

Abstract In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient…

混杂变量非可忽略缺失下中介分析的有效非参数推断AI 解读

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem. In this paper, we propose a general shadow variable framework for identifying mediation effects, allowing shadow…

时空极端事件的模式AI 解读

In environmental science applications, extreme events frequently exhibit a complex spatio-temporal structure, which is difficult to describe flexibly and estimate in a computationally efficient way using state-of-art parametric extreme-value models. In this paper, we propose a computationally-cheap non-parametric approach to investigate the probability distribution of temporal clusters of spatial extremes, and study within-cluster patterns with respect to various…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

基于极小极大缺损的高维假设最优自适应检验AI 解读

The detection boundary is a tool for power evaluation of a high dimensional test, which provides a binary phase transition of power in terms of signal density and strength. However, it cannot separate the L2 and higher criticism (HC) tests under dense signals, and the L∞ and HC tests under highly sparse signals as they share the same detection boundary. This paper proposes minimax relative deficiency and minimax absolute deficiency as sharper measures for power evaluation…

基于二阶特征的局部平稳时间序列结构分类AI 解读

Abstract Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a numerically efficient, practically competitive, and theoretically rigorous classification method for distinguishing between two classes of locally stationary time series based on their time-domain, second-order characteristics. Our approach builds on the autoregressive approximation for locally stationary time series, imposes no requirement on…

高维极值图结构学习AI 解读

Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal variograms, for which we prove non-asymptotic concentration bounds that hold under general domain of attraction conditions. For the popular class of Hüsler--Reiss models, we propose a majority voting algorithm for learning the underlying graph from data through $L^1$ regularized optimization. Our…

自动调参的回火哈密顿蒙特卡洛：高维多峰分布采样AI 解读

Abstract Hamiltonian Monte Carlo (HMC) is widely used for sampling from high-dimensional target distributions with densities known up to proportionality. While HMC exhibits favourable scaling properties in high dimensions, it struggles with strongly multimodal distributions. Tempering methods are commonly used to address multimodality, but they can be difficult to tune, especially in high-dimensional settings. In this study, we propose a method that combines tempering with…

利用数字孪生微观模拟与 Q 学习制定公共卫生政策：新冠加强针案例AI 解读

The COVID-19 pandemic highlighted the urgent need for effective vaccine policies, but traditional clinical trials often lack sufficient data to capture the diverse population characteristics necessary for comprehensive public health strategies. Ethical concerns around randomized trials during a pandemic further complicate policy development for public health. Reinforcement Learning (RL) offers a promising alternative for vaccine policy development. However, direct online RL…

私有线性回归对噪声尾部的适应AI 解读

While the traditional goal of statistics is to infer population parameters, modern practice increasingly demands protection of individual privacy. One way to address this need is to adapt classical statistical procedures into privacy-preserving algorithms. In this article, we develop differentially private tail-robust methods for linear regression. The tradeoff among bias, privacy, and robustness is controlled by a tunable robustification parameter in the Huber loss. We…

主成分空间中的高维回归与维数增益：再考察AI 解读

Benefits of exploiting sparsity in the space of principal components have been well documented both empirically and theoretically (Lang and Zou; Silin and Fan). In this article, we further reveal another unexpected advantage of exploiting sparsity in the space of principal components when the data are contaminated by measurement errors. Assuming the coefficient vector resides in an lq ball (0≤q≤1), we show that an l1 penalized principal components regression has a prediction…

多元时间序列频域中的局部化稀疏主成分分析AI 解读

Principal component analysis has been a main tool in multivariate analysis for estimating a low dimensional linear subspace that explains most of the variability in the data. However, in high-dimensional regimes, naive estimates of the principal loadings are not consistent and difficult to interpret. In the context of time series, principal component analysis of spectral density matrices can provide valuable, parsimonious information about the behavior of the underlying…

流式数据高维广义线性模型的自适应去偏 LassoAI 解读

Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This article introduces a novel approach to online inference in high-dimensional generalized linear models, where we update regression coefficient estimates and their standard errors upon each new data arrival. In contrast to existing methods that either require full dataset access or large-dimensional summary…