521期

最新文章

• Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models AI翻译
  作者：Devijver, Emilie; Gallopin, Melina
  作者单位：KU Leuven; KU Leuven; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom SudParis
  摘要：Gaussian graphical models are widely used to infer and visualize networks of dependencies between continuous variables. However, inferring the graph is difficult when the sample size is small compared to the number of variables. To reduce the number of parameters to estimate in the model, we propose a nonasymptotic model selection procedure supported by strong theoretical guarantees based on an oracle type inequality and a minimax lower bound. The covariance matrix of the model is approximated...
• Compression and Conditional Emulation of Climate Model Output AI翻译
  作者：Guinness, Joseph; Hammerling, Dorit
  作者单位：North Carolina State University; National Center Atmospheric Research (NCAR) - USA
  摘要：Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus it is important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distr...
• Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide Rejoinder AI翻译
  作者：Cressie, Noel
  作者单位：University of Wollongong
• Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series AI翻译
  作者：Lee, Chung Eun; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：In this article, we introduce a new methodology to perform dimension reduction for a stationary multivariate time series. Our method is motivated by the consideration of optimal prediction and focuses on the reduction of the effective dimension in conditional mean of time series given the past information. In particular, we seek a contemporaneous linear transformation such that the transformed time series has two parts with one part being conditionally mean independent of the past. To achieve ...
• A General Framework for Estimation and Inference From Clusters of Features AI翻译
  作者：Reid, Stephen; Taylor, Jonathan; Tibshirani, Robert
  作者单位：Stanford University
  摘要：Applied statistical problems often come with prespecified groupings to predictors. It is natural to test for the presence of simultaneous group-wide signal for groups in isolation, or for multiple groups together. Current tests for the presence of such signals include the classical F-test or a t-test on unsupervised group prototypes (either group centroids or first principal components). In this article, we propose test statistics that aim for power improvements over these classical approaches...
• Minimax Optimal Procedures for Locally Private Estimation Comment AI翻译
  作者：Roth, Aaron
  作者单位：University of Pennsylvania
• Correlated Random Measures AI翻译
  作者：Ranganath, Rajesh; Blei, David M.
  作者单位：Princeton University; Columbia University; Columbia University
  摘要：We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process-based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical model, and these assumpt...
• ECA: High-Dimensional Elliptical Component Analysis in Non-Gaussian Distributions AI翻译
  作者：Han, Fang; Liu, Han
  作者单位：University of Washington; University of Washington Seattle; Princeton University
  摘要：We present a robust alternative to principal component analysis (PCA)called elliptical component analysis (ECA)for analyzing high-dimensional, elliptically distributed data. ECA estimates the eigenspace of the covariance matrix of the elliptical data. To cope with heavy-tailed elliptical distributions, a multivariate rank statistic is exploited. At the model-level, we consider two settings: either that the leading eigenvectors of the covariance matrix are nonsparse or that they are sparse. Met...
• Minimax Optimal Procedures for Locally Private Estimation Rejoinder AI翻译
  作者：Duchi, John C.; Jordan, Michael I.; Wainwright, Martin J.
  作者单位：Stanford University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
• Analysis of Gap Times Based on Panel Count Data With Informative Observation Times and Unknown Start Time AI翻译
  作者：Ma, Ling; Sundaram, Rajeshwari
  作者单位：National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
  摘要：In biomedical studies, one is often interested in repeat events with longitudinal observations occurring only intermittently, resulting in panel count data. The first stage of labor, measured through unit-increments of cervical dilation in pregnant women, provides such an example. Obstetricians are interested in assessing the gap time distribution of per-unit increments of cervical dilation for better management of labor process. Typically, only intermittent medical examinations for cervical d...