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Difference between revisions of "Cramér theorem"

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An integral limit theorem for the probability of large deviations of sums of independent random variables. Let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270001.png" /> be a sequence of independent random variables with the same non-degenerate distribution function <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270002.png" />, such that <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270003.png" /> and such that the generating function <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270004.png" /> of the moments is finite in some interval <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270005.png" /> (this last condition is known as the Cramér condition). Let
  
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<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270006.png" /></td> </tr></table>
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If <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270007.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270008.png" /> as <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c0270009.png" />, then
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<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700010.png" /></td> </tr></table>
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<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700011.png" /></td> </tr></table>
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Here <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700012.png" /> is the normal <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700013.png" /> distribution function and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700014.png" /> is the so-called Cramér series, the coefficients of which depend only on the moments of the random variable <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700015.png" />; this series is convergent for all sufficiently small <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/c/c027/c027000/c02700016.png" />. Actually, the original result, obtained by H. Cramér in 1938, was somewhat weaker than that just described.
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====References====
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<table><TR><TD valign="top">[1]</TD> <TD valign="top">  H. Cramér,  "Sur un nouveau théorème-limite de la théorie des probabilités" , ''Act. Sci. et Ind.'' , '''736''' , Hermann  (1938)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top">  I.A. Ibragimov,  Yu.V. Linnik,  "Independent and stationary sequences of random variables" , Wolters-Noordhoff  (1971)  (Translated from Russian)</TD></TR><TR><TD valign="top">[3]</TD> <TD valign="top">  V.V. Petrov,  "Sums of independent random variables" , Springer  (1975)  (Translated from Russian)</TD></TR></table>
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====Comments====
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See also [[Limit theorems|Limit theorems]]; [[Probability of large deviations|Probability of large deviations]].
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====References====
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<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  R.S. Ellis,  "Entropy, large deviations, and statistical mechanics" , Springer  (1985)</TD></TR></table>

Revision as of 19:21, 7 February 2012

An integral limit theorem for the probability of large deviations of sums of independent random variables. Let be a sequence of independent random variables with the same non-degenerate distribution function , such that and such that the generating function of the moments is finite in some interval (this last condition is known as the Cramér condition). Let

If , as , then

Here is the normal distribution function and is the so-called Cramér series, the coefficients of which depend only on the moments of the random variable ; this series is convergent for all sufficiently small . Actually, the original result, obtained by H. Cramér in 1938, was somewhat weaker than that just described.

References

[1] H. Cramér, "Sur un nouveau théorème-limite de la théorie des probabilités" , Act. Sci. et Ind. , 736 , Hermann (1938)
[2] I.A. Ibragimov, Yu.V. Linnik, "Independent and stationary sequences of random variables" , Wolters-Noordhoff (1971) (Translated from Russian)
[3] V.V. Petrov, "Sums of independent random variables" , Springer (1975) (Translated from Russian)


Comments

See also Limit theorems; Probability of large deviations.

References

[a1] R.S. Ellis, "Entropy, large deviations, and statistical mechanics" , Springer (1985)
How to Cite This Entry:
Cramér theorem. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Cram%C3%A9r_theorem&oldid=20882
This article was adapted from an original article by V.V. Petrov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article