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动态矩阵因子模型：协变量驱动的时变载荷AI 解读

This paper proposes a covariate-driven matrix factor model in which the row and column loading matrices evolve as nonparametric functions of an observable time-varying covariate sequence. This flexible specification accommodates dynamic factor loadings driven by the temporal characteristics of the covariate. We develop a nonparametric estimation procedure that combines kernel smoothing with principal component analysis (PCA). Under high-dimensional asymptotics, we establish…

凸成对差分估计量的稳健推断AI 解读

This article develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs with similar covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show that valid Gaussian and bootstrap-based…

利用南极冰芯数据评估温度与冰积累速率的半参数模型AI 解读

In this paper, we present a semiparametric model for describing the effect of temperature on Antarctic ice accumulation on a paleoclimatic time scale. The model is motivated by sharp ups and downs in the rate of ice accumulation apparent from ice core data records, which are synchronous with movements of temperature. We prove consistency of the estimators under reasonable conditions. We conduct extensive simulations to assess the performance of the estimators and their…

基于球散度的条件独立性度量与局部野自助检验AI 解读

Abstract In this paper we introduce a new measure of conditional dependence between two random vectors $ {\boldsymbol{X}} $ and $ {\boldsymbol{Y}} $, given another random vector $ \boldsymbol{Z} $ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose an estimator of the measure using a kernel-averaging technique and derive its asymptotic distribution. Using this estimator, we construct a test for…

随机试验中 Rosenbaum 秩估计量的稳健性与效率：设计视角AI 解读

Summary Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by Rosenbaum (1993) estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum’s estimator is robust…

平稳时间序列自相关的稳健置信区间AI 解读

This paper develops a simple and new approach for testing linear hypotheses about autocorrelations for time series with general stationary serial correlation structures. A practically important special case is the computation of robust confidence intervals for individual autocorrelations that do not require resampling methods. Inference is heteroskedasticity and autocorrelation robust (HAR) and allows innovations to be uncorrelated but not necessarily independent and…

一般未知因子下共同相关效应回归的横截面自助法AI 解读

The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of unobservables might contain…

混合成员模型中混合概率与排序的统计推断AI 解读

Network data is prevalent in numerous big data applications, including economics and health networks, where understanding the latent structure of the network is of prime importance. In this paper, we model the network using the Degree-Corrected Mixed Membership (DCMM) model. In the DCMM model, for each node i, there exists a membership vector πi=(πi(1),πi(2),…,πi(K)), where πi(k) denotes the weight that node i puts in community k. We derive a novel finite-sample expansion…

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…

Bootstrap 模型平均AI 解读

Model averaging has garnered significant attention in recent years for its ability to combine information from multiple models. A critical challenge in frequentist model averaging is determining the appropriate weight vector. The bootstrap method, well-known for its desirable properties, offers a promising solution. In this article, we propose a bootstrap model averaging approach that selects weights by minimizing a bootstrap-based criterion. Notably, our weight selection…

Abstract Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose…

通过逐坐标高斯化检验高维独立性与条件独立性AI 解读

We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. The key idea is simple, i.e., we first transform each component variable to the standard normal via its marginal empirical distribution, and we then test for independence and conditional independence of the transformed random vectors using appropriate L∞-type test statistics. While we are testing…

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…

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…

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…

广义非参数模型中深度神经网络估计量的统计推断AI 解读

While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between estimation errors and inputs to establish the error bound, a condition often violated in…

含局部测量误差的非平稳函数型时间序列结构变化推断AI 解读

Abstract We study the problem of detecting and localizing change points for a general class of locally stationary functional time series. To accommodate the nonstationarity and other possible complex features, such as discontinuous trajectories, and heterogeneous partial measurement error of contemporary functional data, we propose methods that do not rest on the preprocessing techniques of presmoothing and dimension-reduction, which would be less accurate without the…

高斯混合模型的信息矩阵检验AI 解读

We obtain the Information Matrix test for finite Gaussian mixtures exploiting the EM principle, which implies the moments tested are the expectation given the data of the moments one would assess if one knew the latent component each observation belongs to. Simulations indicate analytical expressions for the asymptotic covariance matrix of those moments adjusted for sampling variability in parameter estimators provide reliable finite sample sizes and good power against…

基于积分条件矩的一致卡方设定检验AI 解读

In spite of the omnibus property of integrated conditional moment (ICM) specification tests, they are not commonly used in empirical practice owing to features such as the non-pivotality of the test and the high computational cost of available bootstrap schemes, especially in large samples. This article proposes specification and mean independence tests based on ICM metrics. The proposed test exhibits consistency, asymptotic $\chi ^2$ -distribution under the null hypothesis,…

基于分块极大值估计量的自助法AI 解读

Abstract The block maxima method is a standard approach for analyzing the extremal behaviour of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by considering sliding block maxima instead. However, the asymptotic variance formula for estimators based on sliding block maxima involves an integral over the covariance of a certain family of multivariate extreme value…

工作稳定性对意大利货币贫困的影响：因果小区域估计AI 解读

Job stability – encompassing secure contracts, adequate wages, social benefits, and career opportunities – is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal…

局部平稳过程的贝叶斯非参数谱分析AI 解读

Stationarity plays a pivotal role in time series analysis. It is not only the basis for the derivation of general asymptotic theory but it also allows an efficient analysis in the frequency domain via the Whittle likelihood, based on the asymptotic independence of the Fourier coefficients. However, many regularly sampled data derived from the observation of physical or ecological processes, for instance, are only locally stationary. They exhibit slowly evolving spectra and…

基于 M 分位数的小区域估计量的偏差控制AI 解读

Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in…