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[[File:Law of large numbers.gif|thumb|Plot of <math>S_n/n</math> (red), its variance <math>1/\sqrt{n}</math> given by CLT (blue) and its bound <math>\sqrt{2\log\log n/n}</math> given by LIL (green). Notice the way it randomly switches from the upper bound given by the law of large numbers to the lower bound. Both axes are non-linearly transformed (as explained in figure summary) to make this effect more visible .]]


In [[probability theory]], the '''law of the iterated logarithm''' describes the magnitude of the fluctuations of a [[random walk]]. The original statement of the law of the iterated logarithm is due to [[Aleksandr Khinchin|A. Y. Khinchin]] (1924).<ref>[[Aleksandr Khinchin|A. Khinchine]]. "Über einen Satz der Wahrscheinlichkeitsrechnung", ''Fundamenta Mathematica'', 6:9-20, 1924. ''(The author's name is shown here in an alternate transliteration.)''</ref> Another statement was given by [[Andrey Kolmogorov|A.N. Kolmogorov]] in 1929.<ref>[[Andrey Kolmogorov|A. Kolmogoroff]]. [http://www-gdz.sub.uni-goettingen.de/cgi-bin/digbib.cgi?PPN235181684_0101 "Über das Gesetz des iterierten Logarithmus"]. ''Mathematische Annalen'', 101:126-135, 1929. ''(At the [http://gdz.sub.uni-goettingen.de/en/index.html Göttinger DigitalisierungsZentrum web site])''</ref>


==Statement==
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Let {''Y''<sub>''n''</sub>} be independent, identically distributed [[random variables]] with means zero and unit variances. Let ''S''<sub>''n''</sub> = ''Y''<sub>1</sub> + … + ''Y''<sub>''n''</sub>. Then
: <math>
    \limsup_{n \to \infty} \frac{S_n}{\sqrt{n \log\log n}} = \sqrt{2}, \qquad \text{a.s.},
  </math>
where “log” is the [[natural logarithm]], “lim sup” denotes the [[limit superior]], and “a.s.” stands for “[[almost surely]]”.<ref>[[Leo Breiman]]. ''Probability''. Original edition published by Addison-Wesley, 1968; reprinted by Society for Industrial and Applied Mathematics, 1992. ''(See Sections 3.9, 12.9, and 12.10; Theorem 3.52 specifically.)''</ref>
<ref>Varadhan, S. R. S. Stochastic processes. Courant Lecture Notes in Mathematics, 16. Courant Institute of Mathematical Sciences, New York; American Mathematical Society, Providence, RI, 2007. </ref>
 
 
==Discussion==
The law of iterated logarithms operates “in between” the [[law of large numbers]] and the [[central limit theorem]]. There are two versions of the law of large numbers — [[weak law of large numbers|the weak]] and [[strong law of large numbers|the strong]] — and they both claim that the sums ''S''<sub>''n''</sub>, scaled by ''n''<sup>−1</sup>, converge to zero, respectively [[convergence of random variables#Convergence_in_probability|in probability]] and [[convergence of random variables#Almost_sure_convergence|almost surely]]:
: <math>
    \frac{S_n}{n} \ \xrightarrow{p}\ 0, \qquad
    \frac{S_n}{n} \ \xrightarrow{a.s.} 0, \qquad \text{as}\ \ n\to\infty.
  </math>
 
On the other hand, the central limit theorem states that the sums ''S''<sub>''n''</sub> scaled by the factor ''n''<sup>−½</sup> converge in distribution to a standard normal distribution. By [[Kolmogorov's zero-one law]], for any fixed ''M'', the probability that the event
<math> \limsup_n \frac{S_n}{\sqrt{n}} > M </math>
occurs is 0 or 1.
Then
 
: <math> P(\limsup_n \frac{S_n}{\sqrt{n}} > M) \geq \limsup_n P(\frac{S_n}{\sqrt{n}} > M) = P(\mathcal{N}(0, 1) > M) > 0</math>
 
so <math>
\limsup_n \frac{S_n}{\sqrt{n}}=\infty
</math>
with probability 1. An identical argument shows that <math>
\liminf_n \frac{S_n}{\sqrt{n}}=-\infty
</math> with probability 1 as well. This implies that these quantities converge neither in probability nor almost surely:
: <math>
    \frac{S_n}{\sqrt n} \ \stackrel{p}{\nrightarrow}\ \forall, \qquad
    \frac{S_n}{\sqrt n} \ \stackrel{a.s.}{\nrightarrow}\ \forall, \qquad \text{as}\ \ n\to\infty.
  </math>
 
The ''law of the iterated logarithm'' provides the scaling factor where the two limits become different:
: <math>
    \frac{S_n}{\sqrt{n\log\log n}} \ \xrightarrow{p}\ 0, \qquad
    \frac{S_n}{\sqrt{n\log\log n}} \ \stackrel{a.s.}{\nrightarrow}\ 0, \qquad \text{as}\ \ n\to\infty.
  </math>
 
Thus, although the quantity <math>S_n/\sqrt{n\log\log n}</math> is less than any predefined ''ε'' > 0 with probability approaching one, that quantity will nevertheless be dropping out of that interval infinitely often, and in fact will be visiting the neighborhoods of any point in the interval (0,√2) almost surely.
 
==Generalizations and variants==
 
The law of the iterated logarithm (LIL) for a sum of independent and identically distributed (i.i.d.) random variables with zero mean and bounded increment dates back to Khintchine and Kolmogorov in the 1920s.
 
Since then, there has been a tremendous amount of work on the LIL for various kinds of
dependent structures and for stochastic processes.  Following is a small sample of notable developments.
 
Hartman-Wintner (1940) generalized LIL to random walks with increments with zero mean and finite variance.
 
Strassen (1964) studied LIL from the point of view of invariance principles.
 
Stout (1970) generalized the LIL to stationary ergodic martingales.
 
Acosta (1983) gave a simple proof of Hartman-Wintner version of LIL.
 
Wittmann (1985) generalized Hartman-Wintner version of LIL to random walks satisfying milder conditions.
 
Vovk (1987) derived a version of LIL valid for a single chaotic sequence (Kolmogorov random sequence). This is notable as it is outside the realm of classical probability theory.
 
 
==See also==
* [[Central limit theorem]]
* [[Law of large numbers]]
* [[Wiener process|Brownian motion]]
 
==Notes==
{{reflist}}
 
[[Category:Probability theory]]
[[Category:Stochastic processes]]
[[Category:Statistical theorems]]

Revision as of 06:48, 18 February 2014


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