2期

最新文章

• Space-time modelling of extreme events AI翻译
  作者：Huser, R.; Davison, A. C.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
• Preadjusted non-parametric estimation of a conditional distribution function AI翻译
  作者：Veraverbeke, Noel; Gijbels, Irene; Omelka, Marek
  作者单位：Hasselt University; North West University - South Africa; KU Leuven; Charles University Prague
• Association pattern discovery via theme dictionary models AI翻译
  作者：Deng, Ke; Geng, Zhi; Liu, Jun S.
  作者单位：Harvard University; Tsinghua University; Peking University
• The joint graphical lasso for inverse covariance estimation across multiple classes AI翻译
  作者：Danaher, Patrick; Wang, Pei; Witten, Daniela M.
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
  摘要：We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes to estimate multiple graphical models that share certain characteristics, such as the locations or weights of non-zero edges. Our approach is based on maximizing a penalized log-likelihood. We employ generalized fused lasso or group lasso penalties and im...
• Regularized matrix regression AI翻译
  作者：Zhou, Hua; Li, Lexin
  作者单位：North Carolina State University
• Two-sample test of high dimensional means under dependence AI翻译
  作者：Cai, T. Tony; Liu, Weidong; Xia, Yin
  作者单位：University of Pennsylvania; Shanghai Jiao Tong University
  摘要：The paper considers in the high dimensional setting a canonical testing problem in multivariate analysis, namely testing the equality of two mean vectors. We introduce a new test statistic that is based on a linear transformation of the data by the precision matrix which incorporates the correlations between the variables. The limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives a...