Stochastic sub-additivity approach to the conditional large deviation principle

Annals of Probability

Volumn 29, Issue 3, 2001, Pages 1303-1328

  Chi, Zhiyi ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Conditional large deviation principle; Mixing conditions; Stochastic approximate sub additivity

Indexed keywords

EID: 0035568568     PISSN: 00911798     EISSN: None     Source Type: Journal    
DOI: 10.1214/aop/1015345604     Document Type: Article

References (12)

1. BREIMAN, L. (1968). Probability. Addison-Wesley, Reading, MA.
2. BRYC, W. and DEMBO, A. (1996). Large deviations and strong mixing. Ann. Inst. H. Poincaré Probab. Statist. 32 549-569.
3. CHI, Z. (2001). The first order asymptotic of waiting times with distortion between stationary processes. IEEE Trans. Inform. Theory 47 338-347.
4. DEMBO, A. and KONTOYIANNIS, I. (1999). The asymptotics of waiting times between stationary processes, allowing distortion. Ann. Appl. Probab. 9 413-429.
5. DEMBO, A. and ZEITOUNI, O. (1992). Large Deviations Techniques and Applications. Jones and Bartlett, Boston.
6. HAMMERSLEY, J. M. (1962). Generalization of the fundamental theorem on sub-additive functions. Proc. Cambridge Philos. Soc. 58 235-238.
7. KONTOYIANNIS, I. (1998). Asymptotic recurrence and waiting times for stationary processes. J. Theoret. Probab. 11 795-811.
8. LUCZAK, T. and SZPANKOWSKI, W. (1997). A suboptimal lossy data compression based on approximate pattern matching. IEEE Trans. Inform. Theory 43 1439-1451.
9. SZPANKOWSKI, W (1993). Asymptotic properties of data compression and suffix trees. IEEE Trans. Inform. Theory 39 1647-1659.
10. WYNER, A. D. and ZIV, J. (1989). Some asymptotic properties of the entropy of a stationary ergodic data source with applications to data compression. IEEE Trans. Inform. Theory 35 1250-1258.
11. YANG, E. and KIEFFER, J. C. (1998). On the performance of data compression algorithms based upon string matching. IEEE Trans. Inform. Theory 44 47-65.
12. YANG, E. and ZHANG, Z. (1999). On the redundancy of lossy source coding with abstract alphabets. IEEE Trans. Inform. Theory 45 1092-1110.