2026

最新文章

• Martin Schlather and Milan Stehlík's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by N. Whiteley et al. AI翻译
  作者：Schlather, Martin; Stehlik, Milan
  作者单位：University of Mannheim; Universidad de Valparaiso
• Beyond the mean: limit theory and tests for infinite-mean autoregressive conditional durations AI翻译
  作者：Cavaliere, Giuseppe; Mikosch, Thomas; Rahbek, Anders; Vilandt, Frederik
  作者单位：University of Bologna; University of Exeter; University of Copenhagen; University of Copenhagen
  摘要：Integrated autoregressive conditional duration (ACD) models serve as counterparts to integrated generalized autoregressive conditional heteroskedastic models used for financial returns. However, despite their resemblance, asymptotic theory for ACD is still incomplete. Central challenges arise from the facts that (i) integrated ACD processes imply durations with infinite expectation and (ii) conventional asymptotic approaches break down due to the randomness in the number of durations within a ...
• Deep learning with missing data AI翻译
  作者：Ma, Tianyi; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science
  摘要：In the context of multivariate nonparametric regression with missing covariates, we propose pattern embedded neural networks (PENNs), which can be applied in conjunction with any existing imputation technique. In addition to a neural network trained on the imputed data, PENNs pass the vectors of observation indicators through a second neural network to provide a compact representation. The outputs are then combined in a third neural network to produce final predictions. Our main theoretical re...
• Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlo AI翻译
  作者：Park, Joonha
  作者单位：University of Kansas
  摘要：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 HMC to enable efficient sampl...
• Modelling with categorical features via exact fusion and sparsity regularization AI翻译
  作者：Behdin, Kayhan; Benbaki, Riade; Radchenko, Peter; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT); University of Sydney; Massachusetts Institute of Technology (MIT)
  摘要：We study the high-dimensional linear regression problem with categorical predictors that have many levels. We propose a new estimation approach, which performs model compression via two mechanisms by simultaneously encouraging (a) clustering of the regression coefficients to collapse some of the categorical levels together; and (b) sparsity of the regression coefficients. We present novel mixed integer programming formulations for our estimator, and develop a custom row generation procedure to...
• Double cross-fit doubly robust estimators: Beyond series regression AI翻译
  作者：McClean, Alec; Balakrishnan, Sivaraman; Kennedy, Edward H.; Wasserman, Larry
  作者单位：Carnegie Mellon University
  摘要：Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effective new estimators for causal functionals. We establish several novel theoretical results for them, building on recent work. We provide a structure-agnostic error analysis, which holds with generic nuisance functions and estimators. Then, we propose n-consistent DCDR estimators with undersmoothed local polynomial regression and k-Nearest Neighbours and a minimax rate-optima...
• Knockoff inference under privacy constraints AI翻译
  作者：Cai, Zhanrui; Fan, Yingying; Gao, Lan
  作者单位：University of Hong Kong; University of Southern California; University of Tennessee System; University of Tennessee Knoxville
  摘要：Model-X knockoff framework offers a model-free variable selection method that ensures finite-sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. We propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preserving the exact F...
• Post-detection inference for sequential changepoint localization AI翻译
  作者：Saha, Aytijhya; Ramdas, Aaditya
  作者单位：Massachusetts Institute of Technology (MIT); Carnegie Mellon University
  摘要：This article addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting inference following a detected change. We develop a very general framework to construct confidence sets for the unknown changepoint using only the data observed up to a data-dependent stopping time at which an arbitrary sequential detection algorithm declares a change. Our framework is nonparametric, making no assumption on the composite postchange class, the observation space, ...
• Junhyung Chang and Xiaoyu Lei's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Chang, Junhyung; Lei, Xiaoyu
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Federated feature selection with false discovery rate control AI翻译
  作者：Hu, Jie; Tong, Jiayi; Ning, Yang; Tang, Cheng Yong; Moore, Jason H.; Li, Runze; Chen, Yong
  作者单位：University of Pennsylvania; Pennsylvania Medicine; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Cornell University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Cedars Sinai Medical Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this paper, we propose Fed-false dis...