Bog, hardback Big and Complex Data Analysis af S. Ejaz Ahmed

Big and Complex Data Analysis (Contributions to Statistics)

- Methodologies and Applications

(Bog, hardback)

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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 longitudin... Læs mere

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Examples incl... Læs mere

Produktdetaljer:

Sprog:
Engelsk
ISBN-13:
9783319415727
Sideantal:
375
Udgivet:
29-03-2017
Udgave:
1st ed. 2017
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Forlagets 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. 56 Tables, color; 55 Illustrations, color; 30 Illustrations, black and white; XIV, 386 p. 85 illus., 55 illus. in color.
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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