2026

最新文章

• Staleness Factors and Volatility Estimation at High Frequencies AI翻译
  作者：Kong, Xinbing; Wu, Bin; Ye, Wuyi
  作者单位：Southeast University - China; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：In this article, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension d and the sampling frequency n tend to in...
• Spatial Scale-Aware Tail Dependence Modeling for High-Dimensional Spatial Extremes AI翻译
  作者：Shi, Muyang; Zhang, Likun; Risser, Mark D.; Shaby, Benjamin A.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Missouri System; University of Missouri Columbia; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
  摘要：Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize dependence in extreme events, we propose a mixture model that achieves flexible dependence properties and allows high-dimensional inference (similar to 600 spatial locations in our data example) for extremes of spatial processes. We modify the popular rando...
• Optimal Plug-in Gaussian Processes for Modeling Derivatives AI翻译
  作者：Liu, Zejian; Li, Meng
  作者单位：Rice University
  摘要：Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating...
• Ball Impurity: Measuring Heterogeneity in General Metric Spaces AI翻译
  作者：Che, Menglu; Li, Ting; Pan, Wenliang; Wang, Xueqin; Zhang, Heping
  作者单位：Yale University; Southern University of Science & Technology; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Data in various domains, such as neuroimaging and network data analysis, often come in complex forms without possessing a Hilbert structure. The complexity necessitates innovative approaches for effective analysis. We propose a novel measure of heterogeneity, ball impurity, which is designed to work with complex non-Euclidean objects. Our approach extends the notion of impurity to general metric spaces, providing a versatile tool for feature selection and tree models. The ball impurity measure...
• Reinforcement Learning with Continuous Actions Under Unmeasured Confounding AI翻译
  作者：Li, Yuhan; Han, Eugene; Hu, Yifan; Zhou, Wenzhuo; Qi, Zhengling; Cui, Yifan; Zhu, Ruoqing
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Irvine; George Washington University; Zhejiang University; Zhejiang University
  摘要：This article addresses the challenge of offline policy learning in continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework. Leveraging this identi...
• Toward Interpretable Deep Generative Models via Causal Representation Learning AI翻译
  作者：Moran, Gemma; Aragam, Bryon
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Chicago
  摘要：Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods' surprising performance is due in part to their ability to learn implicit representations of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to interpret or analyze. To ...
• Efficiency of QMLE for Dynamic Panel Data Models with Interactive Effects AI翻译
  作者：Bai, Jushan
  作者单位：Columbia University
  摘要：This article studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects approach is made. Supp...
• Integrated Path Stability Selection AI翻译
  作者：Melikechi, Omar; Miller, Jeffrey W.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this article, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over th...
• Inside-Out Cross-Covariance for Spatial Multivariate Data AI翻译
  作者：Peruzzi, Michele
  作者单位：University of Michigan System; University of Michigan
  摘要：As the spatial features of multivariate data are increasingly central in researchers' applied problems, there is a growing demand for novel spatially aware methods that are flexible, easily interpretable, and scalable to large data. We develop inside-out cross-covariance (IOX) models for multivariate spatial likelihood-based inference. IOX leads to valid cross-covariance matrix functions which we interpret as inducing spatial dependence on independent replicates of a correlated random vector. ...
• Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework AI翻译
  作者：Shi, Xu; Li, Kendrick Qijun; Yu, Myeonghun; Miao, Wang; Kuchibhotla, Arun Kumar; Hu, Mengtong; Tchetgen Tchetgen, Eric
  作者单位：University of Michigan System; University of Michigan; St Jude Children's Research Hospital; Peking University; Carnegie Mellon University; University of Pennsylvania
  摘要：Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units built to match the treated unit, with weights typically estimated by regressing pretreatment outcomes and measured covariates of the treated unit to those of the control units. However, the classical SC method was primarily proposed for empirical settings where a good pretreatment fit is attainable. In this article, we i...