Random Vectors and Matrices

  • Alan Julian Izenman
Part of the Springer Texts in Statistics book series (STS)


This chapter builds the foundation for the statistical analysis of multivariate data. We first give the notation we use in this book, followed by a quick review of the rules for manipulating vectors and matrices. Then, we learn about random vectors and matrices, which are the fundamental building blocks for multivariate analysis. We then describe the properties of a variety of estimators of an unknown mean vector and unknown covariance matrix of a multivariate Gaussian distribution.


Covariance Matrix Loss Function Random Vector Marginal Distribution Generalize Inverse 
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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Alan Julian Izenman
    • 1
  1. 1.Department of StatisticsTemple UniversityPhiladelphiaUSA

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