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Big and Complex Data Analysis

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Big and Complex Data Analysis
  • Forlagets pris kr. 1.179,95
  • Leveringstid 2-4 uger
  • Forventet levering 24-07-2018
Bog, hardback (kr. 809,95) (kr. 809,95)
  1. Beskrivelse

    This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. 1 Hardback 56 Tables, color; 55 Illustrations, color; 30 Illustrations, black and white; XIV, 386 p. 85 illus., 55 illus. in color.

  2. Yderligere info
    Udgivelsesdato:
    29-03-2017
    Sprog:
    Engelsk
    ISBN13:
    9783319415727
    Sidetal:
    375
    Vægt:
    934 g
    Højde:
    22 mm
    Bredde:
    155 mm
    Længde:
    235 mm
    Mærkat:
    Bog, hardback
    Format:
    Hardback
    Udgave:
    1st ed. 2017
    • Bibliotekernes beskrivelse

      This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field.The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

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