论文检索

流数据非平稳密度核估计的逐块动态平滑参数选择AI 解读

Abstract We consider nonparametric estimation of the nonstationary density of streaming data collected continuously over time. Those types of data are often available only for a limited time after being observed, for example due to storage or data privacy constraints. Therefore, analyzing them requires dynamic approaches that do not require repeated access to past data. A suitable iterative kernel estimator of nonstationary densities has been developed by Rutkowski (1989);…

多因子生产函数协整系统的估计与推断：GDP 与排放的联合建模AI 解读

Abstract This paper develops integrated modified ordinary and generalized least squares estimation for systems of cointegrating multivariate polynomial regressions, i. e., systems of equations that include deterministic variables, integrated processes and (cross-)products of non-negative integer powers of these variables as regressors. The stationary errors are allowed to be correlated across equations, over time and with the regressors. The necessity to consider integrated…

开放集标签偏移数据的半参数学习AI 解读

Abstract We study the open-set label shift problem, where the test data may include a novel class absent from the training data. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric…

用广义似然比检验矩阵因子结构AI 解读

Abstract In this paper, we introduce a generalized likelihood ratio test for assessing whether the factor loading matrix in high-dimensional data exhibits a Kronecker structure, thereby offering a rigorous tool for validating two-way matrix factor models. A central challenge in likelihood-based inference for factor models arises from inherent rotational non-identifiability, which prevents the likelihood function from admitting a standard second-order Taylor expansion. To…

含生成回归元的半参数模型自助设定检验AI 解读

This article provides a specification test for semiparametric models with nonparametrically generated regressors. Such regressors are not observed by the researcher but are nonparametrically identified and estimable. Applications of the test include models with endogenous regressors identified by control functions, semiparametric sample selection models, or binary games with incomplete information. The statistic is built from the residuals of the semiparametric model. A…

何时不应将线性IV估计量解释为LATE？AI 解读

Abstract In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a situation in which additional covariates are required for identification while the reduced-form and first-stage regressions may be misspecified due to an implicit homogeneity restriction on the effects of the instrument. I show that the weights on some conditional LATEs are negative…

高维计数数据的半监督表示学习聚类AI 解读

High-dimensional count data arise in many modern applications, such as electronic health records (EHR) and RNA sequencing, with sparse, heterogeneous observations driven by complex latent structure. These characteristics make modeling and prediction challenging, especially when labels are limited. We propose SCORE, a semi-supervised representation learning framework for clustering and embedding construction from high-dimensional count features. SCORE is built on a…

Perturb-seq 中基于代理变量与工具变量的混杂稳健因果发现与推断AI 解读

Emerging single-cell technologies that combine CRISPR-based genetic perturbations with single-cell RNA sequencing, such as Perturb-seq, offer unprecedented opportunities to uncover cause-and-effect relationships among genes. Nonetheless, Perturb-seq experiments are subject to unobserved factors that, if not properly handled, can severely bias the inferred causal relationships between genes. These latent factors may arise not only from intrinsic molecular features of the…

预印本版本摘要 · 查看来源

面向强超高斯似然的广义切平面近似变分推断框架AI 解读

Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevertheless, existing approaches often depend on either rigid model-specific formulations or stochastic black-box optimization routines. Tangent approximation is a principled class of structured variational methods that exploits the geometry of the underlying probability model. However, its utility has…

摘要暂缺

可加全局Fréchet回归的变量选择AI 解读

We present a novel framework for variable selection in Fréchet regression with responses in general metric spaces, a setting increasingly relevant for analyzing non-Euclidean data such as probability distributions and covariance matrices. Building on the concept of (weak) Fréchet conditional means, we develop an additive regression model that represents the metric-based discrepancy of the response as a sum of covariate-specific nonlinear functions in reproducing kernel…

预印本版本摘要 · 查看来源

度量空间中多元随机过程的幅度与相位变异建模AI 解读

In functional data analysis, distinguishing between amplitude and phase variation is essential to prevent distorted statistical inferences. While extensive work has been devoted to developing time-warping methods to address this issue for Euclidean functional data, much less is known about how to characterize and model these two sources of variability when they co-exist in random processes taking values in general metric spaces. In this paper, we develop a comprehensive…

考虑人口流动的多变量连续处理空气污染研究中的空间干扰方法AI 解读

We develop new methodology to improve our understanding of the causal effects of multivariate air pollution exposures on public health accounting for mobility. Typically, in environmental health studies, exposure to air pollution for an individual is assigned based on their residential address, though many people spend time in different regions with potentially different levels of air pollution. To account for this, we incorporate estimates of the mobility of individuals…

预印本版本摘要 · 查看来源

真实参数与伪真实参数AI 解读

Parameter estimates in misspecified models converge to pseudo-true parameter values, which minimize a population objective function. Pseudo-true values often differ from quantities of economic interest, raising questions of how, if at all, they are relevant for decision-making. To study this question, we consider Bayesian decision-makers facing a linear population minimum distance problem. Within a class of priors motivated by the minimum distance objective, we characterize…

离散抽样连续时间 ARMA 过程的 Phillips–Solo 渐近理论AI 解读

Phillips and Solo (1992, Annals of Statistics 20, 971–2001) provided sets of conditions concerning the summability of coefficients and the properties of innovations in discrete-time linear processes such that normalized sums satisfy certain asymptotic properties, such as laws of large numbers, central limit theorems, and invariance principles. In this article, we consider ARMA processes in continuous time that are sampled at discrete-time intervals and provide conditions on…

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

插件式重抽样的鞍点近似AI 解读

Abstract Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computational cost can be prohibitive. We recognize that several existing procedures belong to a faster plug-in resampling subclass, fixing fitted nuisance parameters during resampling. When the resampled statistic is a sum of conditionally independent terms, the saddlepoint approximation for the resampling p -value offers further acceleration,…

无限阶 U 统计量的刀切经验似然AI 解读

Infinite-order U -statistics play a vital role in modern statistics. However, given their degree k n $k_n$ k Subscript n depends on the sample size n , quantifying their variability is quite challenging, as fixed-degree theories cannot be directly adopted. In this article, we propose a jackknife empirical likelihood (EL) framework that can be used to construct confidence intervals for an infinite-order U -statistic. We establish sufficient conditions for the limiting…

基于流行度回归的潜空间模型：大规模稀疏网络分析AI 解读

Degree heterogeneity is one of the most important properties of network data. It is widely observed that degree heterogeneity is often related to the nodal features. In this study, we investigate the estimation and statistical inference for a popularity regression-based latent space model with nodal features. Given the complex dependence structure induced by the latent space model, we aim to derive analytically tractable objective functions that effectively account for this…

含时间趋势的非线性自回归分布滞后模型的OLS估计与推断AI 解读

This study examines the large-sample behavior of an ordinary least squares (OLS) estimator within a correctly specified nonlinear autoregressive distributed lag (NARDL) model for nonstationary data. Although the OLS estimator suffers from an asymptotically singular matrix problem, it remains consistent for unknown model parameters and asymptotically follows a mixed normal distribution. Additionally, we examine the large-sample behavior of the standard Wald test, as defined…

离散时间与连续时间仿射期权定价模型之间的关系AI 解读

This article studies the weak convergence of discrete-time generalized autoregressive conditional heteroskedasticity (GARCH) and stochastic volatility (SV) option pricing models that allow for fat tails, multi-component volatilities, and non-monotonic pricing kernels. We introduce a general affine framework that enables the simultaneous derivation of new diffusion limits for Gaussian and inverse Gaussian GARCH models based on distributional invariant parametric convergence…

Rao-Blackwell 化的 e-变量AI 解读

Summary We show that for any concave utility, the expected utility of an e-variable can only increase after conditioning on a sufficient statistic, echoing the classic Rao-Blackwell theorem. The simplest form of the result has a straightforward proof using Jensen’s inequality, but we prove more sophisticated versions. We provide several applications of this insight, including a derivation of the log-optimal e-variable for testing the variance in a linear regression model.

马尔可夫采样下随机梯度下降的在线协方差估计AI 解读

We investigate the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. Convergence rates of order $O\big(\sqrt{d}\,n^{-1/8}(\log n)^{1/4}\big)$ and $O\big(\sqrt{d}\,n^{-1/8}\big)$ are established under state-dependent and state-independent Markovian sampling, respectively, where $d$ is the dimensionality and $n$ denotes observations or SGD iterations. These rates match the best-known convergence rate for…

预印本版本摘要 · 查看来源

多因子线性资产定价模型 GMM 估计中的效率与定价精度权衡AI 解读

Even though a multi-factor linear asset pricing model can be equivalently represented in a Beta or in a stochastic discount factor (SDF) form, its inferential efficiency and pricing accuracy features may differ when estimated by the generalized method of moments (GMM), both in small and in large samples. We derive the analytical asymptotic variance of the Beta and the SDF estimators under GMM and find that the SDF approach is likely to be less efficient but to yield more…