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.
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Notes
- 1.
- 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.
The proof is even simpler using the closed half-space upper bound, which is a particular case of (UB cc).
References
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)
R.R. Bahadur, Some Limit Theorems in Statistics (SIAM, Philadelphia, 1971)
R.R. Bahadur, R. Ranga Rao, On deviations of the sample mean. Ann. Math. Stat. 31(4), 1015–1027 (1960)
R.R. Bahadur, S.L. Zabell, Large deviations of the sample mean in general vector spaces. Ann. Probab. 7(4), 587–621 (1979)
P. Bártfai, Large deviations of the sample mean in Euclidean spaces. Mimeograph Series No. 78-13, Statistics Department, Purdue University (1978)
P. Billingsley, Convergence of Probability Measures, 2nd edn. Wiley Series in Probability and Statistics: Probability and Statistics (Wiley, New York, 1999)
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)
R. Cerf, On Cramér’s theory in infinite dimensions. Panoramas et Synthèses 23. Société Mathématique de France, Paris (2007)
R. Cerf, P. Petit, A short proof of Cramér’s theorem in \(\mathbb {R}\). Am. Math. Mon. 118(10), 925–931 (2011)
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)
H. Cramér, Sur un nouveau théorème-limite de la théorie des probabilités. Actual. Sci. Indust. 736, 5–23 (1938)
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)
A. Dembo, O. Zeitouni, Large Deviations Techniques and Applications, 2nd edn. (Springer, New York, 1998). First edition by Jones and Bartlett in 1993
J.D. Deuschel, D.W. Stroock, Large Deviations (Academic, New York, 1989)
I.H. Dinwoodie, Identifying a large deviation rate function. Ann. Probab. 21(1), 216–231 (1993)
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)
N. Dunford, J.T. Schwartz, Linear Operators. Part I: General Theory (Wiley, New York, 1958)
M. Fekete, Über die Verteilung der Wurzeln bei gewissen algebraischen Gleichungen mit. ganzzahligen Koeffizienten. Math. Z. 17, 228–249 (1923)
J.M. Hammersley, Postulates for subadditive processes. Ann. Probab. 2(4), 652–680 (1974)
A.B. Hoadley, On the probability of large deviations of functions of several empirical cdf’s. Ann. Math. Stat. 38, 360–381 (1967)
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
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)
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)
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)
J.J. Moreau, Fonctionnelles convexes. Séminaire sur les Équations aux Dérivées Partielles, Collège de France (1966–1967)
K.R. Parthasarathy, Probability Measures on Metric Spaces (AMS Chelsea Publishing, Providence, 2005). Reprint of the 1967 original
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)
W. Rudin, Functional Analysis (McGraw-Hill, New York, 1973)
D. Ruelle, Correlation functionals. J. Math. Phys. 6(2), 201–220 (1965)
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)
J. Sethuraman, On the probability of large deviations of families of sample means. Ann. Math. Stat. 35, 1304–1316 (1964)
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)
G.L. Sievers, Multivariate probabilities of large deviations. Ann. Stat. 3(4), 897–905 (1975)
S.R.S. Varadhan, Asymptotic probabilities and differential equations. Commun. Pure Appl. Math. 19, 261–286 (1966)
C. Zălinescu, Convex Analysis in General Vector Spaces (World Scientific, River Edge, 2002)
Acknowledgements
I would like to thank Raphaël Cerf and Yann Fuchs for their careful reading, and the referee for his suggestions.
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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
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