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Cantor distribution

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Cantor
Cumulative distribution function
Cumulative distribution function for the Cantor distribution
Parametersnone
SupportCantor set, a subset of [0,1]
PMFnone
CDFCantor function
Mean1/2
Mediananywhere in [1/3, 2/3]
Moden/a
Variance1/8
Skewness0
Excess kurtosis−8/5
MGFet/2k=1cosh(t3k){\displaystyle e^{t/2}\prod _{k=1}^{\infty }\cosh \left({\frac {t}{3^{k}}}\right)}
CFeit/2k=1cos(t3k){\displaystyle e^{it/2}\prod _{k=1}^{\infty }\cos \left({\frac {t}{3^{k}}}\right)}

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.

Characterization

[edit]

Thesupport of the Cantor distribution is theCantor set, itself the intersection of the (countably infinitely many) sets:

C0=[0,1]C1=[0,1/3][2/3,1]C2=[0,1/9][2/9,1/3][2/3,7/9][8/9,1]C3=[0,1/27][2/27,1/9][2/9,7/27][8/27,1/3][2/3,19/27][20/27,7/9][8/9,25/27][26/27,1]C4=[0,1/81][2/81,1/27][2/27,7/81][8/81,1/9][2/9,19/81][20/81,7/27][8/27,25/81][26/81,1/3][2/3,55/81][56/81,19/27][20/27,61/81][62/81,21/27][8/9,73/81][74/81,25/27][26/27,79/81][80/81,1]C5={\displaystyle {\begin{aligned}C_{0}={}&[0,1]\\[8pt]C_{1}={}&[0,1/3]\cup [2/3,1]\\[8pt]C_{2}={}&[0,1/9]\cup [2/9,1/3]\cup [2/3,7/9]\cup [8/9,1]\\[8pt]C_{3}={}&[0,1/27]\cup [2/27,1/9]\cup [2/9,7/27]\cup [8/27,1/3]\cup \\[4pt]{}&[2/3,19/27]\cup [20/27,7/9]\cup [8/9,25/27]\cup [26/27,1]\\[8pt]C_{4}={}&[0,1/81]\cup [2/81,1/27]\cup [2/27,7/81]\cup [8/81,1/9]\cup [2/9,19/81]\cup [20/81,7/27]\cup \\[4pt]&[8/27,25/81]\cup [26/81,1/3]\cup [2/3,55/81]\cup [56/81,19/27]\cup [20/27,61/81]\cup \\[4pt]&[62/81,21/27]\cup [8/9,73/81]\cup [74/81,25/27]\cup [26/27,79/81]\cup [80/81,1]\\[8pt]C_{5}={}&\cdots \end{aligned}}}

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 2t on each one of the 2t intervals.

Moments

[edit]

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:

var(X)=E(var(XY))+var(E(XY))=19var(X)+var{1/6with probability 1/25/6with probability 1/2}=19var(X)+19{\displaystyle {\begin{aligned}\operatorname {var} (X)&=\operatorname {E} (\operatorname {var} (X\mid Y))+\operatorname {var} (\operatorname {E} (X\mid Y))\\&={\frac {1}{9}}\operatorname {var} (X)+\operatorname {var} \left\{{\begin{matrix}1/6&{\mbox{with probability}}\ 1/2\\5/6&{\mbox{with probability}}\ 1/2\end{matrix}}\right\}\\&={\frac {1}{9}}\operatorname {var} (X)+{\frac {1}{9}}\end{aligned}}}

From this we get:

var(X)=18.{\displaystyle \operatorname {var} (X)={\frac {1}{8}}.}

A closed-form expression for any evencentral moment can be found by first obtaining the evencumulants[1]

κ2n=22n1(22n1)B2nn(32n1),{\displaystyle \kappa _{2n}={\frac {2^{2n-1}(2^{2n}-1)B_{2n}}{n\,(3^{2n}-1)}},\,\!}

whereB2n is the 2nthBernoulli number, and thenexpressing the moments as functions of the cumulants.

References

[edit]
  1. ^Morrison, Kent (1998-07-23)."Random Walks with Decreasing Steps"(PDF). Department of Mathematics, California Polytechnic State University. Archived fromthe original(PDF) on 2015-12-02. Retrieved2007-02-16.

Further reading

[edit]
  • Hewitt, E.; Stromberg, K. (1965).Real and Abstract Analysis. Berlin-Heidelberg-New York: Springer-Verlag.This, as with other standard texts, has the Cantor function and its one sided derivates.
  • Hu, Tian-You; Lau, Ka Sing (2002). "Fourier Asymptotics of Cantor Type Measures at Infinity".Proc. AMS. Vol. 130, no. 9. pp. 2711–2717.This is more modern than the other texts in this reference list.
  • Knill, O. (2006).Probability Theory & Stochastic Processes. India: Overseas Press.
  • Mattilla, P. (1995).Geometry of Sets in Euclidean Spaces. San Francisco: Cambridge University Press.This has more advanced material on fractals.
Discrete
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