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| Cantor | |||
|---|---|---|---|
Cumulative distribution function | |||
| Parameters | none | ||
| Support | Cantor set, a subset of [0,1] | ||
| PMF | none | ||
| CDF | Cantor function | ||
| Mean | 1/2 | ||
| Median | anywhere in [1/3, 2/3] | ||
| Mode | n/a | ||
| Variance | 1/8 | ||
| Skewness | 0 | ||
| Excess kurtosis | −8/5 | ||
| MGF | |||
| CF | |||
TheCantor distribution is theprobability distribution whosecumulative distribution function is theCantor function.
This distribution has neither aprobability density function nor aprobability mass function, since although its cumulative distribution function is acontinuous function, the distribution is notabsolutely continuous with respect toLebesgue measure, nor does it have any point-masses. It is thus neither a discrete nor an absolutely continuous probability distribution, nor is it a mixture of these. Rather it is an example of asingular distribution.
Its cumulative distribution function is continuous everywhere but horizontal almost everywhere, so is sometimes referred to as theDevil's staircase, although that term has a more general meaning.
Thesupport of the Cantor distribution is theCantor set, itself the intersection of the (countably infinitely many) sets:
The Cantor distribution is the unique probability distribution for which for anyCt (t ∈ { 0, 1, 2, 3, ... }), the probability of a particular interval inCt containing the Cantor-distributed random variable is identically 2−t on each one of the 2t intervals.
It is easy to see by symmetry and being bounded that for arandom variableX having this distribution, itsexpected value E(X) = 1/2, and that all odd central moments ofX are 0.
Thelaw of total variance can be used to find thevariance var(X), as follows. For the above setC1, letY = 0 ifX ∈ [0,1/3], and 1 ifX ∈ [2/3,1]. Then:
From this we get:
A closed-form expression for any evencentral moment can be found by first obtaining the evencumulants[1]
whereB2n is the 2nthBernoulli number, and thenexpressing the moments as functions of the cumulants.