525期

最新文章

• Discussion of PENCOMP AI翻译
  作者：Antonelli, Joseph; Daniels, Michael J.
  作者单位：State University System of Florida; University of Florida
• Comment on Penalized Spline of Propensity Methods for Treatment Comparison by Zhou, Elliott, and Little AI翻译
  作者：Spieker, Andrew J.
  作者单位：Vanderbilt University
• Discussion of Penalized Spline of Propensity Methods for Treatment Comparison by Zhou, Elliott, and Little AI翻译
  作者：Yang, Shu; Zeng, Donglin
  作者单位：North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
• A Computational Framework for Multivariate Convex Regression and Its Variants AI翻译
  作者：Mazumder, Rahul; Choudhury, Arkopal; Iyengar, Garud; Sen, Bodhisattva
  作者单位：Massachusetts Institute of Technology (MIT); University of North Carolina; University of North Carolina Chapel Hill; Columbia University; Columbia University
  摘要：We study the nonparametric least squares estimator (LSE) of a multivariate convex regression function. The LSE, given as the solution to a quadratic program with O(n(2)) linear constraints (n being the sample size), is difficult to compute for large problems. Exploiting problem specific structure, we propose a scalable algorithmic framework based on the augmented Lagrangian method to compute the LSE. We develop a novel approach to obtain smooth convex approximations to the fitted (piecewise af...
• FreSpeD: Frequency-Specific Change-Point Detection in Epileptic Seizure Multi-Channel EEG Data AI翻译
  作者：Schroder, Anna Louise; Ombao, Hernando
  作者单位：University of London; London School Economics & Political Science; King Abdullah University of Science & Technology
  摘要：The goal in this article is to develop a practical tool that identifies changes in the brain activity as recorded in electroencephalograms (EEG). Our method is devised to detect possibly subtle disruptions in normal brain functioning that precede the onset of an epileptic seizure. Moreover, it is able to capture the evolution of seizure spread from one region (or channel) to another. The proposed frequency-specific change-point detection method (FreSpeD) deploys a cumulative sum-type test stat...
• Least Ambiguous Set-Valued Classifiers With Bounded Error Levels AI翻译
  作者：Sadinle, Mauricio; Lei, Jing; Wasserman, Larry
  作者单位：University of Washington; University of Washington Seattle; Carnegie Mellon University
  摘要：In most classification tasks, there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framework for multiclass set-valued classification, where the classifiers guarantee user-defined levels of coverage or confidence (the probability that the true label is contained in t...
• Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models AI翻译
  作者：Guo, Zijian; Wang, Wanjie; Cai, T. Tony; Li, Hongzhe
  作者单位：Rutgers University System; Rutgers University New Brunswick; National University of Singapore; University of Pennsylvania; University of Pennsylvania
  摘要：Estimating the genetic relatedness between two traits based on the genome-wide association data is an important problem in genetics research. In the framework of high-dimensional linear models, we introduce two measures of genetic relatedness and develop optimal estimators for them. One is genetic covariance, which is defined to be the inner product of the two regression vectors, and another is genetic correlation, which is a normalized inner product by their lengths. We propose functional de-...
• Functional Graphical Models AI翻译
  作者：Qiao, Xinghao; Guo, Shaojun; James, Gareth M.
  作者单位：University of London; London School Economics & Political Science; Renmin University of China; University of Southern California
  摘要：Graphical models have attracted increasing attention in recent years, especially in settings involving high-dimensional data. In particular, Gaussian graphical models are used to model the conditional dependence structure among multiple Gaussian random variables. As a result of its computational efficiency, the graphical lasso (glasso) has become one of the most popular approaches for fitting high-dimensional graphical models. In this paper, we extend the graphical models concept to model the ...
• Confidence Sets for Phylogenetic Trees AI翻译
  作者：Willis, Amy
  作者单位：University of Washington; University of Washington Seattle
  摘要：Inferring evolutionary histories (phylogenetic trees) has important applications in biology, criminology, and public health. However, phylogenetic trees are complex mathematical objects that reside in a non-Euclidean space, which complicates their analysis. While our mathematical, algorithmic, and probabilistic understanding of phylogenies in their metric space is mature, rigorous inferential infrastructure is as yet undeveloped. In this manuscript, we unify recent computational and probabilis...
• Linear Non-Gaussian Component Analysis Via Maximum Likelihood AI翻译
  作者：Risk, Benjamin B.; Matteson, David S.; Ruppert, David
  作者单位：Emory University; Cornell University
  摘要：Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a linear non-Gaussian com...