Skip to main content

Cramér’s Theorem in Banach Spaces Revisited

  • Chapter
  • First Online:
Séminaire de Probabilités XLIX

Part of the book series: Lecture Notes in Mathematics ((SEMPROBAB,volume 2215))

  • 725 Accesses

Abstract

The text summarizes the general results of large deviations for empirical means of independent and identically distributed variables in a separable Banach space, without the hypothesis of exponential tightness. The large deviation upper bound for convex sets is proved in a nonasymptotic form; as a result, the closure of the domain of the entropy coincides with the closed convex hull of the support of the common law of the variables. Also a short original proof of the convex duality between negentropy and pressure is provided: it simply relies on the subadditive lemma and Fatou’s lemma, and does not resort to the law of large numbers or any other limit theorem. Eventually a Varadhan-like version of the convex upper bound is established and embraces both results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Hypothesis (\(\hat {\mathbf {C}}\)) of [14] and Assumption 6.1.2 (b) of [13] were introduced so as to complete the proofs of Appendix of [4], but they are not required to prove the first proposition of the appendix.

  2. 2.

    Physically speaking, the function p should be interpreted as the opposite of a free energy, which is proportional to the pressure in the case of simple fluids.

  3. 3.

    The proof is even simpler using the closed half-space upper bound, which is a particular case of (UB cc).

References

  1. R. Azencott, Grandes déviations et applications, in École d’Été de Probabilités de Saint-Flour VIII-1978. Lecture Notes in Mathematics, vol. 774 (Springer, Berlin, 1980)

    Google Scholar 

  2. R.R. Bahadur, Some Limit Theorems in Statistics (SIAM, Philadelphia, 1971)

    Google Scholar 

  3. R.R. Bahadur, R. Ranga Rao, On deviations of the sample mean. Ann. Math. Stat. 31(4), 1015–1027 (1960)

    Google Scholar 

  4. R.R. Bahadur, S.L. Zabell, Large deviations of the sample mean in general vector spaces. Ann. Probab. 7(4), 587–621 (1979)

    Google Scholar 

  5. P. Bártfai, Large deviations of the sample mean in Euclidean spaces. Mimeograph Series No. 78-13, Statistics Department, Purdue University (1978)

    Google Scholar 

  6. P. Billingsley, Convergence of Probability Measures, 2nd edn. Wiley Series in Probability and Statistics: Probability and Statistics (Wiley, New York, 1999)

    Google Scholar 

  7. A.A. Borovkov, B.A. Rogozin, О щентральной прещельной теореме в многомерном случае. Teor. Verojatnost. i Primenen. 10(1), 61–69 (1965). English translation: On the multi-dimensional central limit theorem. Theory Probab. Appl. 10(1), 55–62 (1965)

    Google Scholar 

  8. R. Cerf, On Cramér’s theory in infinite dimensions. Panoramas et Synthèses 23. Société Mathématique de France, Paris (2007)

    Google Scholar 

  9. R. Cerf, P. Petit, A short proof of Cramér’s theorem in \(\mathbb {R}\). Am. Math. Mon. 118(10), 925–931 (2011)

    Google Scholar 

  10. H. Chernoff, A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Ann. Math. Stat. 23, 493–507 (1952)

    Google Scholar 

  11. H. Cramér, Sur un nouveau théorème-limite de la théorie des probabilités. Actual. Sci. Indust. 736, 5–23 (1938)

    Google Scholar 

  12. A. De Acosta, On large deviations of sums of independent random vectors, in Probability in Banach Spaces V. Lecture Notes in Mathematics, vol. 1153 (Springer, Berlin, 1985)

    Google Scholar 

  13. A. Dembo, O. Zeitouni, Large Deviations Techniques and Applications, 2nd edn. (Springer, New York, 1998). First edition by Jones and Bartlett in 1993

    Google Scholar 

  14. J.D. Deuschel, D.W. Stroock, Large Deviations (Academic, New York, 1989)

    Google Scholar 

  15. I.H. Dinwoodie, Identifying a large deviation rate function. Ann. Probab. 21(1), 216–231 (1993)

    Google Scholar 

  16. M.D. Donsker, S.R.S. Varadhan, Asymptotic evaluation of certain Markov process expectations for large time III. Commun. Pure Appl. Math. 29, 389–461 (1976)

    Google Scholar 

  17. N. Dunford, J.T. Schwartz, Linear Operators. Part I: General Theory (Wiley, New York, 1958)

    Google Scholar 

  18. M. Fekete, Über die Verteilung der Wurzeln bei gewissen algebraischen Gleichungen mit. ganzzahligen Koeffizienten. Math. Z. 17, 228–249 (1923)

    Google Scholar 

  19. J.M. Hammersley, Postulates for subadditive processes. Ann. Probab. 2(4), 652–680 (1974)

    Google Scholar 

  20. A.B. Hoadley, On the probability of large deviations of functions of several empirical cdf’s. Ann. Math. Stat. 38, 360–381 (1967)

    Google Scholar 

  21. W. Hoeffding, On probabilities of large deviations, in Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, 1965/1966. Vol. I: Statistics (University of California Press, Berkeley, 1967), pp. 203–219

    Google Scholar 

  22. J.F.C. Kingman, Subadditive Processes, in École d’Été de Probabilités de Saint-Flour V-1975. Lecture Notes in Mathematics, vol. 539 (Springer, Berlin, 1976)

    Google Scholar 

  23. O.E. Lanford, Entropy and equilibrium states in classical statistical mechanics, in Statistical Mechanics and Mathematical Problems. Lecture Notes in Physics, vol. 20 (Springer, Berlin, 1973)

    Google Scholar 

  24. J.T Lewis, C.E. Pfister, W.G. Sullivan, Entropy, concentration of probability and conditional limit theorems. Markov Processes Relat. Fields 1(3), 319–386 (1995)

    Google Scholar 

  25. J.J. Moreau, Fonctionnelles convexes. Séminaire sur les Équations aux Dérivées Partielles, Collège de France (1966–1967)

    Google Scholar 

  26. K.R. Parthasarathy, Probability Measures on Metric Spaces (AMS Chelsea Publishing, Providence, 2005). Reprint of the 1967 original

    Google Scholar 

  27. V.V. Petrov, О вероятностях больших уклонений сумм независимюх случайнюх величин. Teor. Verojatnost. i Primenen 10(2), 310–322 (1965). English translation: On the probabilities of large deviations for sums of independent random variables. Theory Probab. Appl. 10(2), 287–298 (1965)

    Google Scholar 

  28. W. Rudin, Functional Analysis (McGraw-Hill, New York, 1973)

    MATH  Google Scholar 

  29. D. Ruelle, Correlation functionals. J. Math. Phys. 6(2), 201–220 (1965)

    Article  MathSciNet  Google Scholar 

  30. I.N. Sanov, О вероятности больших отклонений случайнюх величин. Mat. Sb. 42(1), 11–44 (1957). English translation: On the probability of large deviations of random variables. Sel. Transl. Math. Statist. Probab. I, 213–244 (1961)

    Google Scholar 

  31. J. Sethuraman, On the probability of large deviations of families of sample means. Ann. Math. Stat. 35, 1304–1316 (1964)

    Article  MathSciNet  Google Scholar 

  32. J. Sethuraman, On the probability of large deviations of the mean for random variables in D[0, 1]. Ann. Math. Stat. 36, 280–285 (1965)

    Article  MathSciNet  Google Scholar 

  33. G.L. Sievers, Multivariate probabilities of large deviations. Ann. Stat. 3(4), 897–905 (1975)

    Article  MathSciNet  Google Scholar 

  34. S.R.S. Varadhan, Asymptotic probabilities and differential equations. Commun. Pure Appl. Math. 19, 261–286 (1966)

    Article  MathSciNet  Google Scholar 

  35. C. Zălinescu, Convex Analysis in General Vector Spaces (World Scientific, River Edge, 2002)

    Book  Google Scholar 

Download references

Acknowledgements

I would like to thank Raphaël Cerf and Yann Fuchs for their careful reading, and the referee for his suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pierre Petit .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Petit, P. (2018). Cramér’s Theorem in Banach Spaces Revisited. In: Donati-Martin, C., Lejay, A., Rouault, A. (eds) Séminaire de Probabilités XLIX. Lecture Notes in Mathematics(), vol 2215. Springer, Cham. https://doi.org/10.1007/978-3-319-92420-5_12

Download citation

Publish with us

Policies and ethics