544期

最新文章

• Robust Estimation of Large Panels with Factor Structures AI翻译
  作者：Avarucci, Marco; Zaffaroni, Paolo
  作者单位：University of Glasgow; Imperial College London; Sapienza University Rome
  摘要：This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain circumstances, because it ne...
• Bias-Adjusted Spectral Clustering in Multi-Layer Stochastic Block Models AI翻译
  作者：Lei, Jing; Lin, Kevin Z.
  作者单位：Carnegie Mellon University; University of Pennsylvania
  摘要：We consider the problem of estimating common community structures in multi-layer stochastic block models, where each single layer may not have sufficient signal strength to recover the full community structure. In order to efficiently aggregate signal across different layers, we argue that the sum-of-squared adjacency matrices contain sufficient signal even when individual layers are very sparse. Our method uses a bias-removal step that is necessary when the squared noise matrices may overwhel...
• Confidence Intervals for Discrete Data in Clinical Research AI翻译
  作者：Agresti, Alan
  作者单位：State University System of Florida; University of Florida
• Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covariance Matrices with Application to Gene-Set Testing AI翻译
  作者：Yu, Xiufan; Li, Danning; Xue, Lingzhou; Li, Runze
  作者单位：University of Notre Dame; Northeast Normal University - China; Northeast Normal University - China; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Power-enhanced tests with high-dimensional data have received growing attention in theoretical and applied statistics in recent years. Existing tests possess their respective high-power regions, and we may lack prior knowledge about the alternatives when testing for a problem of interest in practice. There is a critical need of developing powerful testing procedures against more general alternatives. This article studies the joint test of two-sample mean vectors and covariance matrices for hig...
• Bayesian Modeling of Sequential Discoveries AI翻译
  作者：Zito, Alessandro; Rigon, Tommaso; Ovaskainen, Otso; Dunson, David B.
  作者单位：Duke University; University of Milano-Bicocca; University of Jyvaskyla; University of Helsinki; Norwegian University of Science & Technology (NTNU)
  摘要：We aim at modeling the appearance of distinct tags in a sequence of labeled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarized via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modeling by directly specifying the probability of a new discovery, therefore, allowing for ...
• Online Bootstrap Inference For Policy Evaluation In Reinforcement Learning AI翻译
  作者：Ramprasad, Pratik; Li, Yuantong; Yang, Zhuoran; Wang, Zhaoran; Sun, Will Wei; Cheng, Guang
  作者单位：Purdue University System; Purdue University; University of California System; University of California Los Angeles; Yale University; Northwestern University; Purdue University System; Purdue University
  摘要：The recent emergence of reinforcement learning (RL) has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing methods for inference in online learning are restricted to settings involving independently sampled observations, while inference methods in RL have so far been limited to the batch setting. The bootstrap is a flexible and efficient approach for statistical inference in online learning algorithms, but its efficac...
• Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections AI翻译
  作者：Salvana, Mary Lai O.; Lenzi, Amanda; Genton, Marc G.
  作者单位：King Abdullah University of Science & Technology
  摘要：When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special property is observed: the covariances and cross-covariances are stronger in certain directions. This property is attributed to the presence of natural forces, such as wind, which cause the transport and dispersion of these variables. This spatio-temporal dynamics prompted the use of the Lagrangian reference frame alon...
• Sharp Nonparametric Bounds for Decomposition Effects with Two Binary Mediators AI翻译
  作者：Gabriel, Erin E.; Sachs, Michael C.; Sjolander, Arvid
  作者单位：University of Copenhagen; Karolinska Institutet
  摘要：In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathway between the randomized treatment and the outcome. In the setting with two sequential mediators, there are a variety of decompositions of the total risk difference into mediation effects. We derive sharp and valid bounds for a number of mediation effects in the setting of two sequential mediators both with unmeasure...
• A General Pairwise Comparison Model for Extremely Sparse Networks AI翻译
  作者：Han, Ruijian; Xu, Yiming; Chen, Kani
  作者单位：Chinese University of Hong Kong; Utah System of Higher Education; University of Utah; Hong Kong University of Science & Technology
  摘要：Statistical estimation using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this article, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in terms of model parameterization. Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in term...
• A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions AI翻译
  作者：Zeng, Yanyan; Pang, Daolin; Zhao, Hongyu; Wang, Tao
  作者单位：Shanghai Jiao Tong University; Yale University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
  摘要：High throughput sequencing data collected to study the microbiome provide information in the form of relative abundances and should be treated as compositions. Although many approaches including scaling and rarefaction have been proposed for converting raw count data into microbial compositions, most of these methods simply return zero values for zero counts. However, zeros can distort downstream analyses, and they can also pose problems for composition-aware methods. This problem is exacerbat...