rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Analysis for High-Dimensional Data
    sub title: The Abel Symposium 2014
  name:
    name part: Frigessi, Arnoldo.
    role:
      role term: editor.
  name:
    name part: Bühlmann, Peter.
    role:
      role term: editor.
  name:
    name part: Glad, Ingrid K.
    role:
      role term: editor.
  name:
    name part: Langaas, Mette.
    role:
      role term: editor.
  name:
    name part: Richardson, Sylvia.
    role:
      role term: editor.
  name:
    name part: Vannucci, Marina.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    edition: 1st ed. 2016.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 306 p. 65 illus., 19 illus. in color. online resource.
  abstract: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.
  table of contents: Some Themes in High-Dimensional Statistics: A. Frigessi et al -- Laplace Appoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton et al -- Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration: L.C. Bergersen, I. Glad et al -- Spectral Clustering and Block Models: a Review and a new Algorithm: S. Bhattacharyya et al -- Bayesian Hierarchical Mixture Models: L. Bottelo et al -- iBATCGH; Integrative Bayesian Analysis of Transcriptomic and CGH Data: Cassese, M. Vannucci et al -- Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West -- Combining Single and Paired End RNA-seq Data for Differential Expression Analysis: F. Feng, T.Speed et al -- An Imputation Method for Estimation the Learning Curve in Classification Problems: E. Laber et al -- Baysian Feature Allocation Models for Tumor Heterogeneity: J. Lee, P. Mueller et al -- Bayesian Penalty Mixing: The Case of a Non-Separable Penalty: V. Rockova et al -- Confidence Intervals for Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al -- Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al. .
  note: edited by Arnoldo Frigessi, Peter Bühlmann, Ingrid K. Glad, Mette Langaas, Sylvia Richardson, Marina Vannucci.
  subject:
    topic: Mathematics
  subject:
    topic: Bioinformatics
  subject:
    topic: Computer mathematics
  subject:
    topic: Statistics
  subject:
    topic: Mathematics
  subject:
    topic: Computational Mathematics and Numerical Analysis
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Bioinformatics
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  classification: QA71-90
  classification: 518
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Abel Symposia, 11
  identifier: 9783319270999
  identifier: http://dx.doi.org/10.1007/978-3-319-27099-9
  record info:
    record creation date: 160216
    record change date: 20160217151325.0
    record identifier: 978-3-319-27099-9