2026

最新文章

• Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation AI翻译
  作者：Zhao, Boxin; Ma, Cong; Kolar, Mladen
  作者单位：University of Chicago; University of Chicago; University of Southern California; Mohamed bin Zayed University of Artificial Intelligence MBZUAI
  摘要：Precision matrix estimation is essential in various fields, yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation. First, we obtain initial estimators using a multi-task learning objective that captures both shared and unique features across studies. Then, we refine these estimators through ...
• Estimating Multiple Structural Breaks in Large Panels With Unobserved Heterogeneity AI翻译
  作者：Huang, Wenxin; Sun, Yucheng; Wang, Yiru
  作者单位：Shanghai Jiao Tong University; Capital University of Economics & Business; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：This article proposes a novel algorithm to identify potentially multiple breaks in linear panel data models, while the slope coefficients can have individual heterogeneity and the cross-sectional dimension is allowed to diverge jointly with the time span. The algorithm adopts a shrinkage penalty to detect breaks, and delivers a consistent estimator for the number of breaks and fraction-consistent estimators for break dates. In the presence of latent grouped heterogeneity, we employ the classif...
• Spatial Variation on Multiple Scales in Line Transect Data; the Case of Antarctic Fin Whales AI翻译
  作者：Breivik, Olav Nikolai; Skaug, Hans J.; Jullum, Martin; Biuw, Martin
  作者单位：University of Bergen; Institute of Marine Research - Norway; Institute of Marine Research - Norway
  摘要：Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes are modeled as a t...
• Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions AI翻译
  作者：Kock, Anders B.; Pedersen, Rasmus S.; Sorensen, Jesper R. -V.
  作者单位：University of Oxford; University of Copenhagen; Danish Finance Institute
  摘要：Lasso-type estimators are routinely used to estimate high-dimensional time series models. The theoretical guarantees established for these estimators typically require the penalty level to be chosen in a suitable fashion often depending on unknown population quantities. Furthermore, the resulting estimates and the number of variables retained in the model depend crucially on the chosen penalty level. However, there is currently no theoretically founded guidance for this choice in the context o...
• Bayesian Transfer Learning for Enhanced Estimation and Inference AI翻译
  作者：Lai, Daoyuan; Madrid Padilla, Oscar Hernan; Li, Xiang; Gu, Tian
  作者单位：Columbia University; Columbia University; University of California System; University of California Los Angeles; University of Texas System; UTMD Anderson Cancer Center
  摘要：Transfer learning enhances model performance in a target population with limited samples by leveraging knowledge from related studies. While many works focus on improving predictive performance, challenges persist in statistical inference. Bayesian approaches naturally provide uncertainty quantification for parameter estimates; however, existing Bayesian transfer learning methods are typically limited to single-source scenarios or require individual-level data. We introduce TRansfer leArning v...
• Structured Mixture of Continuation-Ratio Logits Models for Ordinal Regression AI翻译
  作者：Kang, Jizhou; Kottas, Athanasios
  作者单位：University of California System; University of California Santa Cruz
  摘要：We develop a nonparametric Bayesian modeling approach to ordinal regression based on priors placed directly on the discrete distribution of the ordinal responses. The prior probability models are built from a structured mixture of multinomial distributions. We leverage the continuation-ratio logits representation to formulate the mixture kernel, with mixture weights defined through the logit stick-breaking process that incorporates the covariates through a linear function. The implied regressi...
• Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data AI翻译
  作者：Han, Ruijian; Luo, Lan; Luo, Yuanhang; Lin, Yuanyuan; Huang, Jian
  作者单位：Hong Kong Polytechnic University; Rutgers University System; Chinese University of Hong Kong; Hong Kong Polytechnic University; Hong Kong Polytechnic University
  摘要：Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This article introduces a novel approach to online inference in high-dimensional generalized linear models, where we update regression coefficient estimates and their standard errors upon each new data arrival. In contrast to existing methods that either require full dataset access or large-dimensional summary statistics storage,...
• Cluster Quilting: Spectral Clustering for Patchwork Learning AI翻译
  作者：Zheng, Lili; Chang, Andersen; Allen, Genevera I.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Baylor College of Medicine; Columbia University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limitations and measurement expenses, such patchwork data structures are frequently seen in applications like neuroscience, healthcare, and genomics, among others. Instead of analyzing each data patch separately, it is highly desirable to extract comprehensive knowledge from the whole dataset. In this work, we focus on the clusteri...
• A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems AI翻译
  作者：Menicali, Luca; Grace, Andrew P.; Richter, David H.; Castruccio, Stefano
  作者单位：University of Notre Dame; University of Notre Dame
  摘要：Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-B & eacute;nard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recur...
• Learning with the Minimum Description Length Principle AI翻译
  作者：Grunwald, Peter D.
  作者单位：Centrum Wiskunde & Informatica (CWI); Leiden University - Excl LUMC; Leiden University