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Higher moments of Banach space valued random variables


About this Title

Svante Janson and Sten Kaijser

Publication: Memoirs of the American Mathematical Society
Publication Year: 2015; Volume 238, Number 1127
ISBNs: 978-1-4704-1465-8 (print); 978-1-4704-2617-0 (online)
DOI: http://dx.doi.org/10.1090/memo/1127
Published electronically: May 14, 2015
Keywords:Banach space valued random variable, projective tensor product, injective tensor product, Bochner integral, Dunford integral, Pettis integral, approximation property, $D[0, 1]$

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Table of Contents


Chapters

  • Chapter 1. Introduction
  • Chapter 2. Preliminaries
  • Chapter 3. Moments of Banach space valued random variables
  • Chapter 4. The approximation property
  • Chapter 5. Hilbert spaces
  • Chapter 6. $L^p(\mu )$
  • Chapter 7. $C(K)$
  • Chapter 8. $c_0(S)$
  • Chapter 9. $D[0,1]$
  • Chapter 10. Uniqueness and Convergence
  • Appendix A. The Reproducing Hilbert Space
  • Appendix B. The Zolotarev Distances

Abstract


We define the :th moment of a Banach space valued random variable as the expectation of its :th tensor power; thus the moment (if it exists) is an element of a tensor power of the original Banach space. We study both the projective and injective tensor products, and their relation. Moreover, in order to be general and flexible, we study three different types of expectations: Bochner integrals, Pettis integrals and Dunford integrals. One of the problems studied is whether two random variables with the same injective moments (of a given order) necessarily have the same projective moments; this is of interest in applications. We show that this holds if the Banach space has the approximation property, but not in general. Several chapters are devoted to results in special Banach spaces, including Hilbert spaces, and . The latter space is non-separable, which complicates the arguments, and we prove various preliminary results on e.g. measurability in that we need. One of the main motivations of this paper is the application to Zolotarev metrics and their use in the contraction method. This is sketched in an appendix.

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