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High-Dimensional Data Analysis

High-Dimensional Data Analysis

High-Dimensional Data Analysis
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High-Dimensional Data Analysis Hardback -

by T. Tony Cai (Editor); Xiaotong Shen (Editor)

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World Scientific Publishing Company, Incorporated , pp. 320 . Hardback. New.
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Details

  • Title High-Dimensional Data Analysis
  • Author T. Tony Cai (Editor); Xiaotong Shen (Editor)
  • Binding Hardback
  • Edition Hardcover editio
  • Condition New
  • Pages 320
  • Volumes 1
  • Language ENG
  • Publisher World Scientific Publishing Company, Incorporated
  • Publication date pp. 320
  • Features Bibliography, Index, Table of Contents
  • Bookseller's Inventory # 62176869
  • ISBN 9789814324854 / 981432485X
  • Weight 1.6 lbs (0.73 kg)
  • Dimensions 9.4 x 6.8 x 0.9 in (23.88 x 17.27 x 2.29 cm)
  • Category Mathematics
  • Dewey Decimal Code 519
  • Quantity available 1

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Reader reviews for High-Dimensional Data Analysis

From the publisher

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction.

It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research.

The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.

From the jacket flap

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction.

It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research.

The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.

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