New PDF release: High-Dimensional Covariance Estimation: With

By Mohsen Pourahmadi

Methods for estimating sparse and massive covariance matrices

Covariance and correlation matrices play primary roles in each element of the research of multivariate info accrued from various fields together with company and economics, well-being care, engineering, and environmental and actual sciences. High-Dimensional Covariance Estimation provides available and accomplished assurance of the classical and sleek techniques for estimating covariance matrices in addition to their purposes to the speedily constructing parts mendacity on the intersection of facts and computing device learning.

Recently, the classical pattern covariance methodologies were converted and stronger upon to satisfy the wishes of statisticians and researchers facing huge correlated datasets. High-Dimensional Covariance Estimation makes a speciality of the methodologies in line with shrinkage, thresholding, and penalized chance with purposes to Gaussian graphical versions, prediction, and mean-variance portfolio administration. The ebook is based seriously on regression-based principles and interpretations to attach and unify many latest tools and algorithms for the task.

High-Dimensional Covariance Estimation beneficial properties chapters on:

  • Data, Sparsity, and Regularization
  • Regularizing the Eigenstructure
  • Banding, Tapering, and Thresholding
  • Covariance Matrices
  • Sparse Gaussian Graphical Models
  • Multivariate Regression

The publication is a perfect source for researchers in records, arithmetic, enterprise and economics, machine sciences, and engineering, in addition to an invaluable textual content or complement for graduate-level classes in multivariate research, covariance estimation, statistical studying, and high-dimensional info analysis.

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High-Dimensional Covariance Estimation: With High-Dimensional Data (Wiley Series in Probability and Statistics) by Mohsen Pourahmadi

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