High-dimensional covariance estimation

by Pourahmadi, Mohsen

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Title Statements

Full title :High-dimensional covariance estimation / Mohsen Pourahmadi, Department of Statistics, Texas A & M University.

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"--

"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"--

Edition Statements

Publication Date:2013

Publication Statements

Publisher(s):Wiley
Place of Publication:Hoboken, New Jersey
Language:eng
Publication Date:2013

The Physical Object

Description:x, 184 pages : : illustrations ; : 25 cm.

Edition Identifiers

ISBN:9781118034293, 1118034295
oclc:ocn833144808
LC Classification:QA279
Dewey Decimal:519.5/38

Data, sparsity, and regularization -- Covariance matrices -- Regularizing the eigenstructure -- Sparse gaussian graphical models -- Banding, tapering, and thresholding -- Multivariate regression: accounting for correlation.