Abstract
In this chapter we first introduce and describe different symmetrical and asymmetrical parametric and semiparametric (linear) models which are then later used as the model assumptions in the statistical analysis. The models discussed include multivariate normal distribution N p (μ,Σ) and its different extensions including multivariate t distribution t v,p (μ,Σ) distribution, multivariate elliptical distribution (μ,Σ,ρ) as well as still wider semiparametric symmetrical models. Also some models with skew distributions (generalized elliptical model, mixture models, skew-elliptical model, independent component model) are briefly discussed.
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
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Oja, H. (2010). Multivariate location and scatter models. In: Multivariate Nonparametric Methods with R. Lecture Notes in Statistics(), vol 199. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0468-3_2
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0468-3_2
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-0467-6
Online ISBN: 978-1-4419-0468-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)