Cantor distribution

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Cantor
Probability mass function
Cumulative distribution function
Image:CantorFunction.png
Cumulative distribution function of the Cantor distribution
Parameters none
Support Cantor set
Probability mass function (pmf) none
Cumulative distribution function (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
Entropy
Moment-generating function (mgf) e^{t/2} 
                     \prod_{i=1}^{\infty} \cosh{\left(\frac{t}{3^{i}}
                                                \right)}
Characteristic function e^{\mathrm{i}\,t/2} 
                     \prod_{i=1}^{\infty} \cos{\left(\frac{t}{3^{i}}
                                                \right)}

The Cantor distribution is the probability distribution whose cumulative distribution function is the Cantor function.

This distribution has neither a probability density function nor a probability mass function, as it is not absolutely continuous with respect to Lebesgue measure, nor has it any point-masses. It is thus neither a discrete nor a continuous probability distribution, nor is it a mixture of these. Rather it is an example of a singular distribution.

Its cumulative distribution function is sometimes referred to as the Devil's staircase, although that term has a more general meaning.

Characterization

The support of the Cantor distribution is the Cantor set, itself the (countably infinite) intersection of the sets


\begin{align}
 C_{0} = & [0,1] \\
 C_{1} = & [0,1/3]\cup[2/3,1] \\
 C_{2} = & [0,1/9]\cup[2/9,1/3]\cup[2/3,7/9]\cup[8/9,1] \\
 C_{3} = & [0,1/27]\cup[2/27,1/9]\cup[2/9,7/27]\cup[8/27,1/3]\cup \\
         & [2/3,19/27]\cup[20/27,7/9]\cup[8/9,25/27]\cup[26/27,1] \\
 C_{4} = & \cdots .
\end{align}

The Cantor distribution is the unique probability distribution for which for any Ct (t ∈ { 0, 1, 2, 3, ... }), the probability of a particular interval in Ct containing the Cantor-distributed random variable is identically 2-t on each one of the 2t intervals.

Moments

It is easy to see by symmetry that for a random variable X having this distribution, its expected value E(X) = 1/2, and that all odd central moments of X are 0.

The law of total variance can be used to find the variance var(X), as follows. For the above set C1, let Y = 0 if X ∈ [0,1/3], and 1 if X ∈ [2/3,1]. Then:


\begin{align}
\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{align}

From this we get:

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

A closed form expression for any even central moment can be found by first obtaining the even cumulants[1]


 \kappa_{2n} = \frac{2^{2n-1} (2^{2n}-1) B_{2n}}
                    {n (3^{2n}-1)},

where B2n is the 2nth Bernoulli number, and then expressing the moments as functions of the cumulants.

External links

it:Variabile casuale di Cantor

Acknowledgement and Attribution Regarding Sources of Content

Some of the initial content on this page may be incorporated in part from copyleft sources in the public domain including wikis such as Wikipedia and AskDrWiki. Drug information for patients came from the The National Library of Medicine. Infectious disease information may have come from the Centers for Disease Control (CDC). Differential Diagnoses are drawn from clinicians as well as an amalgamation of 3 sources: 1.The Disease Database; 2. Kahan, Scott, Smith, Ellen G. In A Page: Signs and Symptoms. Malden, Massachusetts: Blackwell Publishing, 2004:3; 3. Sailer, Christian, Wasner, Susanne. Differential Diagnosis Pocket. Hermosa Beach, CA: Borm Bruckmeir Publishing LLC, 2002:7 .

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