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高维时间序列的频域统计推断AI 解读

Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters like the spectral density matrix and its inverse, the coherence or the partial coherence, encode comprehensively the complex linear relations between the component processes of the multivariate system. In this paper, we develop inference procedures for such parameters in a high-dimensional, time…

快速信号区域检测及其全基因组关联研究应用AI 解读

Research on the localization of the genetic basis associated with diseases or traits has been widely conducted in the last few decades. Scan methods have been developed for region-based analysis in whole-genome association studies, helping us better understand how genetics influences human diseases or traits, especially when the aggregated effects of multiple causal variants are present. In this paper, we propose a fast and effective algorithm coupling with high-dimensional…

基于弱依赖随机过程极大值的高维变量聚类AI 解读

We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm depending on a tuning parameter…

高维期望短缺回归AI 解读

Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools for learning the relationship between a response variable and a set of covariates while exploring the heterogeneous effects of the covariates. In the health disparity research, for example, the lower/upper tail of the conditional distribution of a health-related outcome, given high-dimensional…

目标处理效应的联邦自适应因果估计AI 解读

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level…

时间序列数据的高维 Knockoffs 推断AI 解读

We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in Barber, Candès, and Samworth to the time series setting to relax the assumption of known covariate…

联邦元学习的稳健推断AI 解读

Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints due to privacy concerns. In this paper, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the…

高维数据的自适应检验AI 解读

In this article, we propose a class of Lq-norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficients, and testing of component-wise independence for high-dimensional observations, among others. Under the null hypothesis, we derive asymptotic normality and independence between Lq-norm based U-statistics for several qs under mild…

采用惩罚最大似然的反卷积密度估计AI 解读

Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all infinite-dimensional deconvolution methods in the literature use Fourier transformations. These methods are mathematically neat, but unstable, and produce bad estimates when signal-noise ratio or sample size are low. A popular alternative is to maximize penalized likelihood for a finite-dimensional basis expansion of the…

跨维度马尔可夫链蒙特卡洛算法的几何遍历性AI 解读

This article studies the convergence properties of trans-dimensional MCMC algorithms when the total number of models is finite. It is shown that, for reversible and some non-reversible trans-dimensional Markov chains, under mild conditions, geometric convergence is guaranteed if the Markov chains associated with the within-model moves are geometrically ergodic. This result is proved in an L2 framework using the technique of Markov chain decomposition. While the technique was…

矩阵 GARCH 模型：推断与应用AI 解读

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use…

神经网络菲利普斯曲线与深度产出缺口AI 解读

Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. First,…

高维部分线性测量误差模型的校准均衡估计与双重选择AI 解读

In practice, measurement error data is frequently encountered and needs to be handled appropriately. As a result of additional bias induced by measurement error, many existing estimation methods fail to achieve satisfactory performances. This paper studies high-dimensional partially linear measurement error models. It proposes a calibrated equilibrium (CARE) estimation method to calibrate the bias caused by measurement error and overcomes the technical difficulty of the…

重新审视依赖性：时间序列最可预测的方面AI 解读

Serial dependence and predictability are two sides of the same coin. The literature has considered alternative measures of these two fundamental concepts. In this article, we aim to distill the most predictable aspect of a univariate time series, that is, the one for which predictability is optimized. Our target measure is the mutual information between the past and future of a random process, a broad measure of predictability that takes into account all future forecast…

含高维协变量的惩罚稀疏协方差回归AI 解读

Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i.e., similarity matrices). Specifically, we use the penalization method to identify the informative predictors and estimate their associated coefficients simultaneously. We first investigate the Lasso estimator and subsequently…

区组随机实验与再随机化实验中 Lasso 调整的设计型理论AI 解读

Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions…

概率型分位数因子分析AI 解读

This article extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of low, medium, and high economic policy uncertainty, as well as…

均值泛函估计的改进分治方法及其平均处理效应应用AI 解读

Mean estimation is an important issue in statistical inference and machine learning. We are concerned with estimating mean functional that is a function of several nonparametric functions when there is a large amount of observations. Directly estimating such mean functional through nonparametric smoothing has the complexity of at least a quadratic order of the sample size, which is computationally prohibitive for massive data. The divide-and-conquer approach are thus readily…

一般缺失模式下高维因子模型的推断AI 解读

This paper establishes the inferential theory for the least squares estimation of large factor models with missing data. We propose a unified framework for asymptotic analysis of factor models that covers a wide range of missing patterns, including heterogenous random missing, selection on covariates/factors/loadings, block/staggered missing, mixed frequency and ragged edge. We establish the average convergence rates of the estimated factor space and loading space, the limit…

不依赖维度区分的变点检测AI 解读

Change point testing for high-dimensional data has attracted a lot of attention in statistics, econometrics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than the sample size but is not small, it is often unclear whether a method that is tailored to high-dimensional data or simply a classical method that is developed and justified for low-dimensional data is preferred. In…

信任作为进入壁垒：金融科技采用的证据AI 解读

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in credit markets. The empirical setting exploits the outbreak of the Wells Fargo scandal as a negative shock to borrowers’ trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political…

一步平滑样条工具变量回归AI 解读

Summary We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one step and relies on a unique regularisation parameter. We derive rates of the convergence for the estimator and its first derivative, which are uniform in the support of the endogenous variable. We also address the issue of imposing monotonicity in estimation and extend…

信息性抽样下的半参数自适应估计AI 解读

In probability sampling, sampling weights are often used to remove selection bias in the sample. The Horvitz–Thompson estimator is well known to be consistent and asymptotically normally distributed; however, it is not necessarily efficient. This study derives the semiparametric efficiency bound for various target parameters by considering the survey weights as random variables and consequently proposes two semiparametric estimators with working models on the survey weights.…

用于高斯近似的自归一化 Cramér 型中等偏差定理AI 解读

Berry–Esseen type bounds for Gaussian approximation of standardized sums have been extensively studied under exponential type moment conditions. In this paper, a Cramér type moderate deviation theorem is established for self-normalized Gaussian approximation under finite moment conditions. More specifically, let X1,X2,…,Xn be i.i.d. Rp-valued random vectors with zero means. Let Sn,j=∑ i=1nXij and Vn,j2=∑i=1nXij2. We show that if the correlation matrix of X1 is Ip and the…