Kolmogorov's inequality

From Wikipedia, the free encyclopedia

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.^{[citation needed]}

1 Statement of the inequality
2 Proof
3 See also
4 References

[edit] Statement of the inequality

Let X₁, ..., 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,

$\Pr \left(\max_{1\leq k\leq n} | S_k |\geq\lambda\right)\leq \frac{1}{\lambda^2} \operatorname{Var} [S_n] \equiv \frac{1}{\lambda^2}\sum_{k=1}^n \operatorname{Var}[X_k],$

where S_k = X₁ + ... + X_k.

[edit] Proof

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

$Z_{i+1} = \left\{ \begin{array}{ll} S_{i+1} & \text{ if } \displaystyle \max_{1 \leq j \leq i} S_j < \lambda \\ Z_i & \text{ otherwise} \end{array} \right.$

for all $i$ . Then $(Z_i)_{i=0}^n$ is a also a martingale. Since $E[S i] = E[S i - 1]$ for all $i$ and $E[E[X | Y]] = E[X]$ by the law of total expectation,

$\begin{align} \sum_{i=1}^n \text{E}[ (S_i - S_{i-1})^2] &= \sum_{i=1}^n \text{E}[ S_i^2 - 2 S_i S_{i-1} + S_{i-1}^2 ] \\ &= \sum_{i=1}^n \text{E}\left[ S_i^2 - 2 \text{E}[ S_i S_{i-1} | S_{i-1} ] + \text{E}[S_{i-1}^2 | S_{i-1}] \right] \\ &= \sum_{i=1}^n \text{E}\left[ S_i^2 - 2 \text{E}[ S^2_{i-1} | S_{i-1} ] + \text{E}[S_{i-1}^2 | S_{i-1}] \right] \\ &= \text{E}[S_n^2] - \text{E}[S_0^2] = \text{E}[S_n^2]. \end{align}$

The same is true for $(Z_i)_{i=0}^n$ . Thus

$\begin{align} \text{Pr}\left( \max_{1 \leq i \leq n} S_i \geq \lambda\right) &= \text{Pr}[Z_n \geq \lambda] \\ &\leq \frac{1}{\lambda^2} \text{E}[Z_n^2] =\frac{1}{\lambda^2} \sum_{i=1}^n \text{E}[(Z_i - Z_{i-1})^2] \\ &\leq \frac{1}{\lambda^2} \sum_{i=1}^n \text{E}[(S_i - S_{i-1})^2] =\frac{1}{\lambda^2} \text{E}[S_n^2] = \frac{1}{\lambda^2} \text{Var}[S_n]. \end{align}$

by Chebyshev's inequality.

[edit] See also

[edit] References

Billingsley, Patrick (1995). Probability and Measure. New York: John Wiley & Sons, Inc.. ISBN 0-471-00710-2. (Theorem 22.4)
Feller, William [1950] (1968). An Introduction to Probability Theory and its Applications, Vol 1, Third Edition (in English), New York: John Wiley & Sons, Inc., xviii+509. ISBN 0-471-25708-7.

This article incorporates material from Kolmogorov's inequality on PlanetMath, which is licensed under the GFDL.