Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Izenman, A.J. (2013). Random Vectors and Matrices. In: Modern Multivariate Statistical Techniques. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78189-1_3
Download citation
DOI: https://doi.org/10.1007/978-0-387-78189-1_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-78188-4
Online ISBN: 978-0-387-78189-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)