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Shrinkage Estimation for Mean and Covariance MatricesMatrix Algebra

Shrinkage Estimation for Mean and Covariance Matrices: Matrix Algebra [Matrix algebra is an important step in mathematical treatment of shrinkage estimation for matrix parameters, and in particular the Moore-Penrose inverse and some matrix decompositions are required for defining matricial shrinkage estimators. This chapter first explains the notation used in this book and subsequently lists helpful results in matrix algebra.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Shrinkage Estimation for Mean and Covariance MatricesMatrix Algebra

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Publisher
Springer Singapore
Copyright
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
ISBN
978-981-15-1595-8
Pages
7 –12
DOI
10.1007/978-981-15-1596-5_2
Publisher site
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Abstract

[Matrix algebra is an important step in mathematical treatment of shrinkage estimation for matrix parameters, and in particular the Moore-Penrose inverse and some matrix decompositions are required for defining matricial shrinkage estimators. This chapter first explains the notation used in this book and subsequently lists helpful results in matrix algebra.]

Published: Apr 17, 2020

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