DOI QR코드

DOI QR Code

THE SECOND CENTRAL LIMIT THEOREM FOR MARTINGALE DIFFERENCE ARRAYS

  • Bae, Jongsig (Department of Mathematics Institute of Basic Science Sungkyunkwan University) ;
  • Jun, Doobae (Department of Mathematics(and RING) Gyeongsang National University) ;
  • Levental, Shlomo (Department of Statistics and Probability Michigan State University)
  • Received : 2011.08.12
  • Published : 2014.03.31

Abstract

In Bae et al. [2], we have considered the uniform CLT for the martingale difference arrays under the uniformly integrable entropy. In this paper, we prove the same problem under the bracketing entropy condition. The proofs are based on Freedman inequality combined with a chaining argument that utilizes majorizing measures. The results of present paper generalize those for a sequence of stationary martingale differences. The results also generalize independent problems.

Keywords

References

  1. J. Bae and S. Levental, Uniform CLT for Markov chains and its invariance principle: A Martingale approach, J. Theoret. Probab. 8 (1995), no. 3, 549-570. https://doi.org/10.1007/BF02218044
  2. J. Bae, D. Jun, and S. Levental, The uniform CLT for Martingale difference arrays under the uniformly integrable entropy, Bull. Korean Math. Soc. 47 (2010), no. 1, 39-51. https://doi.org/10.4134/BKMS.2010.47.1.039
  3. R. M. Dudley, A Course on Empirical Processes, Lecture notes in Math. 1097, Springer-Verlag, New York. 1984.
  4. R. M. Dudley, Uniform Central Limit Theorems, Cambridge Studies in Advanced Mathematics 63, Cambridge University Press, Cambridge, 1999.
  5. D. Freedman, On tail probabilities for Martingales, Ann. Probab. 3 (1975), 100-118. https://doi.org/10.1214/aop/1176996452
  6. M. Ossiander, A central limit theorem under metric entropy with $L_2$ bracketing, Ann. Probab. 15 (1987), no. 3, 897-919. https://doi.org/10.1214/aop/1176992072
  7. D. Pollard, Empirical Processes: Theory and Applications, Regional conference series in Probability and Statistics 2, Inst. Math. Statist., Hayward, CA. 1990.
  8. S. van der Geer, Empirical Processes in M-Estimation, Cambridge Series in Statistical and Probabilistic Mathematics. 2000.
  9. A. W. Van der Vaart and J. A. Wellner, Weak Convergence and Empirical Processes with Applications to Statistics, Springer series in Statistics, Springer-Verlag, New York, 1996.