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Multivariate Statistical Models

  • David Ruppert
  • David S. Matteson
Part of the Springer Texts in Statistics book series (STS)

Abstract

Often we are not interested merely in a single random variable but rather in the joint behavior of several random variables, for example, returns on several assets and a market index. Multivariate distributions describe such joint behavior. This chapter is an introduction to the use of multivariate distributions for modeling financial markets data. Readers with little prior knowledge of multivariate distributions may benefit from reviewing Appendices A.12–A.14 before reading this chapter.

Keywords

Covariance Matrix Random Vector Positive Semidefinite Fisher Information Matrix Multivariate Normal Distribution 
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.

References

  1. Azzalini, A. (2014) The Skew-Normal and Related Families (Institute of Mathematical Statistics Monographs, Book 3), Cambridge University Press.Google Scholar
  2. Azzalini, A., and Capitanio, A. (2003) Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t distribution. Journal of the Royal Statistics Society, Series B, 65, 367–389.CrossRefzbMATHMathSciNetGoogle Scholar
  3. Lehmann, E. L. (1999) Elements of Large-Sample Theory, Springer-Verlag, New York.CrossRefzbMATHGoogle Scholar
  4. van der Vaart, A. W. (1998) Asymptotic Statistics, Cambridge University Press, Cambridge.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David Ruppert
    • 1
  • David S. Matteson
    • 2
  1. 1.Department of Statistical Science and School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

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