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High-dimensional covariance estimation / Mohsen Pourahmadi.

By: Material type: TextTextSeries: Wiley series in probability and statisticsPublication details: New Jersey : John Wiley, c2013.Description: x, 184 p. : illustrations ; 25 cmISBN:
  • 9781118034293 (hardback)
Subject(s): Additional physical formats: Online version:: Modern methods to covariance estimationDDC classification:
  • 000SA.069 23 P877
Contents:
1. Introduction-- 2. Data, sparsity, and regularization-- 3. Covariance matrices-- 4. Regularizing the eigenstructure-- 5. Sparse gaussian graphical models-- 6. Banding, tapering, and thresholding-- 7. Multivariate regression: accounting for correlation-- Bibliography-- Index.
Summary: "Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site"--Summary: "The aim of this book is to provide computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data"--
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.069 P877 (Browse shelf(Opens below)) Available 135824
Total holds: 0

Includes bibliographical references (pages 171-179) and index.

1. Introduction--
2. Data, sparsity, and regularization--
3. Covariance matrices--
4. Regularizing the eigenstructure--
5. Sparse gaussian graphical models--
6. Banding, tapering, and thresholding--
7. Multivariate regression: accounting for correlation--
Bibliography--
Index.

"Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site"--

"The aim of this book is to provide computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data"--

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