Acta Mathematica Hungarica

, Volume 65, Issue 1, pp 1–16 | Cite as

Strong laws of large numbers for arrays of orthogonal random elements in Banach spaces

  • F. Móricz
  • Kuo-Liang Su
  • R. L. Taylor
Article

Keywords

Banach Space Random Element 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. O. Howell and R. L. Taylor, Marcinkiewicz-Zygmund weak laws of large numbers for unconditional random elements in Banach spaces, inProc. Conf. Probability in Banach Spaces (Medford, 1980), Lect. Notes in Math. Vol. 860, Springer-Verlag (1981), pp. 219–230.Google Scholar
  2. [2]
    F. Móricz, Strong limit theorems for quasi-orthogonal random fields,J. Multivariate Analysis,30 (1989), 255–278.Google Scholar
  3. [3]
    F. Móricz, Moment inequalities and the strong laws of large numbers.Z. Wahrschein. Verw. Gebiete,35 (1976), 299–314.Google Scholar
  4. [4]
    F. Móricz, SLLN and convergence rates for nearly orthogonal sequences of random variables,Proc. Amer. Math. Soc.,95 (1985), 287–294.Google Scholar
  5. [5]
    F. Móricz and R. L. Taylor, Strong laws of large numbers fro arrays of orthogonal random variables,Math. Nachrichten,141 (1989), 145–152.Google Scholar
  6. [6]
    R. L. Taylor,Stochastic Convergence of Weighted Sums of Random Elements in Linear Spaces, Lect. Notes in Math., Vol. 672, Springer-Verlag, (New York, 1978).Google Scholar
  7. [7]
    P. Warren and J. Howell, A strong law of large numbers for orthogonal Banach spacevalued random variables, inProbability in Banach Spaces (Oberwolfach, 1975), Lect. Notes in Math., Vol. 526, Springer-Verlag (New York, 1975), pp. 253–262.Google Scholar

Copyright information

© Akadémiai Kiadó 1994

Authors and Affiliations

  • F. Móricz
    • 1
  • Kuo-Liang Su
    • 2
  • R. L. Taylor
    • 3
  1. 1.University of SzegedBolyai InstituteSzegedHungary
  2. 2.Department of BusinessNational Open UniversityTaichungTaiwan
  3. 3.Department of StatisticsUniversity of GeorgiaAthensUSA

Personalised recommendations