Eaton's inequality: Difference between revisions
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A related bound is Edelman's |
A related bound is Edelman's |
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<math> P( | \sum_{ i = 1 }^n a_i X_i | \ge k ) \le 2 ( 1 - \Phi[ k - \frac{ 1.5 }{ k } ] ) = 2 |
<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> |
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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 ≤ i ≤ n. 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
- ^ Eaton ML (1974) A probability inequality for linear combinations of bounded random variables. Ann Statist 2(3) 609-614