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Eaton's inequality: Difference between revisions

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A related bound is Edelman's
A related bound is Edelman's


<math> P( | \sum_{ i = 1 }^n a_i X_i | \ge k ) \le 2 ( 1 - \Phi[ k - \frac{ 1.5 }{ k } ] ) = 2 B_Ed( k ) </math>
<math> P( | \sum_{ i = 1 }^n a_i X_i | \ge k ) \le 2 ( 1 - \Phi[ k - \frac{ 1.5 }{ k } ] ) = 2 B_{ Ed }( k ) </math>


where ''Φ''( x ) is the normal probability distribution.
where ''Φ''( x ) is the normal probability distribution.

Revision as of 09:22, 24 February 2013

Eaton's inequality is a bound on the maximal values of a linear combination of bounded random variables.

History

This inequality was described in 1974 by Eaton.[1]

Statement of the inequality

Let Xi be a set of real independent random variables each with a mean of zero and bounded by 1 ( | Xi | ≤ 1). Let 1 ≤ in. The variates do not have to be identically or symmetrically distributed. Let ai be a set of n fixed real numbers with

Eaton showed that

where φ( x ) is the normal probability density.

A related bound is Edelman's

where Φ( x ) is the normal probability distribution.

References

  1. ^ Eaton ML (1974) A probability inequality for linear combinations of bounded random variables. Ann Statist 2(3) 609-614