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Multivariate location and scatter models

  • Hannu Oja
Chapter
Part of the Lecture Notes in Statistics book series (LNS, volume 199)

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.

Keywords

Independent Component Analysis Independent Component Analysis Multivariate Normal Distribution Scatter Matrix Scatter Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Tampere School of Public HealthUniversity of TampereTampereFinland

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