Kolmogorov's inequality

Kolmogorov's inequality

In probability theory, Kolmogorov's inequality is a so-called "maximal inequality" that gives a bound on the probability that the partial sums of a finite collection of independent random variables exceed some specified bound. The inequality is named after the Russian mathematician Andrey Kolmogorov.Fact|date=May 2007

tatement of the inequality

Let "X"1, ..., "X""n" : Ω → R be independent random variables defined on a common probability space (Ω, "F", Pr), with expected value E ["X""k"] = 0 and variance Var ["X""k"] < +∞ for "k" = 1, ..., "n". Then, for each λ > 0,

:Pr left(max_{1leq kleq n} | S_k |geqlambda ight)leq frac{1}{lambda^2} operatorname{Var} [S_n] equiv frac{1}{lambda^2}sum_{k=1}^n operatorname{Var} [X_k] ,

where "S""k" = "X"1 + ... + "X""k".


The following argument is due to Kareem Amin and employs discrete martingales. As argued in the discussion of Doob's martingale inequality, the sequence S_1, S_2, dots, S_n is a martingale.
Without loss of generality, we can assume that S_0 = 0 and S_i geq 0 for all i.Define (Z_i)_{i=0}^n as follows. Let Z_0 = 0, and:Z_{i+1} = left{ egin{array}{ll}S_{i+1} & ext{ if } displaystyle max_{1 leq j leq i} S_j < lambda \ Z_i & ext{ otherwise}end{array} ight.for all i.Then (Z_i)_{i=0}^n is a also a martingale. Since ext{E} [S_{i}] = ext{E} [S_{i-1}] for all i and ext{E} [ ext{E} [X|Y] = ext{E} [X] by the law of total expectation,:egin{align}sum_{i=1}^n ext{E} [ (S_i - S_{i-1})^2] &= sum_{i=1}^n ext{E} [ S_i^2 - 2 S_i S_{i-1} + S_{i-1}^2 ] \&= sum_{i=1}^n ext{E}left [ S_i^2 - 2 ext{E} [ S_i S_{i-1} | S_{i-1} ] + ext{E} [S_{i-1}^2 | S_{i-1}] ight] \&= sum_{i=1}^n ext{E}left [ S_i^2 - 2 ext{E} [ S^2_{i-1} | S_{i-1} ] + ext{E} [S_{i-1}^2 | S_{i-1}] ight] \&= ext{E} [S_n^2] - ext{E} [S_0^2] = ext{E} [S_n^2] .end{align}The same is true for (Z_i)_{i=0}^n. Thus:egin{align} ext{Pr}left( max_{1 leq i leq n} S_i geq lambda ight) &= ext{Pr} [Z_n geq lambda] \&leq frac{1}{lambda^2} ext{E} [Z_n^2] =frac{1}{lambda^2} sum_{i=1}^n ext{E} [(Z_i - Z_{i-1})^2] \&leq frac{1}{lambda^2} sum_{i=1}^n ext{E} [(S_i - S_{i-1})^2] =frac{1}{lambda^2} ext{E} [S_n^2] = frac{1}{lambda^2} ext{Var} [S_n] .end{align}by Chebyshev's inequality.

ee also

* Chebyshev's inequality
* Doob's martingale inequality
* Etemadi's inequality
* Landau-Kolmogorov inequality
* Markov's inequality


* (Theorem 22.4)


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