Kumaraswamy distribution

From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses, see Kumaraswamy (disambiguation).
Probability density function
Probability density function
Cumulative distribution function
Cumulative distribution function
Parameters a>0\, (real)
b>0\, (real)
Support x \in [0,1]\,
PDF abx^{a-1}(1-x^a)^{b-1}\,
CDF [1-(1-x^a)^b]\,
Mean \frac{b\Gamma(1+\tfrac{1}{a})\Gamma(b)}{\Gamma(1+\tfrac{1}{a}+b)}\,
Median \left(1-2^{-1/b}\right)^{1/a}
Mode \left(\frac{a-1}{ab-1}\right)^{1/a} for a\geq 1, b\geq 1, (a,b)\neq (1,1)
Variance (complicated-see text)
Skewness (complicated-see text)
Ex. kurtosis (complicated-see text)

In probability and statistics, the Kumaraswamy's double bounded distribution is a family of continuous probability distributions defined on the interval [0,1]. It is similar to the Beta distribution, but much simpler to use especially in simulation studies due to the simple closed form of both its probability density function and cumulative distribution function. This distribution was originally proposed by Poondi Kumaraswamy for variables that are lower and upper bounded.


Probability density function[edit]

The probability density function of the Kumaraswamy distribution is

 f(x; a,b) = a b x^{a-1}{ (1-x^a)}^{b-1},  \ \ \mbox{where} \ \ x \in [0,1],

and where a and b are non-negative shape parameters.

Cumulative distribution function[edit]

The cumulative distribution function is

F(x; a,b)=\int_{0}^{x}f(\xi; a,b) d\xi = 1-(1-x^a)^b.\

Generalizing to arbitrary interval support[edit]

In its simplest form, the distribution has a support of [0,1]. In a more general form, the normalized variable x is replaced with the unshifted and unscaled variable z where:

 x = \frac{z-z_{\text{min}}}{z_{\text{max}}-z_{\text{min}}} , \qquad z_{\text{min}} \le z \le z_{\text{max}}. \,\!


The raw moments of the Kumaraswamy distribution are given by[citation needed]:

m_n = \frac{b\Gamma(1+n/a)\Gamma(b)}{\Gamma(1+b+n/a)} = bB(1+n/a,b)\,

where B is the Beta function. The variance, skewness, and excess kurtosis can be calculated from these raw moments. For example, the variance is:


Relation to the Beta distribution[edit]

The Kuramaswamy distribution is closely related to Beta distribution. Assume that Xa,b is a Kumaraswamy distributed random variable with parameters a and b. Then Xa,b is the a-th root of a suitably defined Beta distributed random variable. More formally, Let Y1,b denote a Beta distributed random variable with parameters \alpha=1 and \beta=b. One has the following relation between Xa,b and Y1,b.


with equality in distribution.

\operatorname{P}\{X_{a,b}\le x\}=\int_0^x ab t^{a-1}(1-t^a)^{b-1}dt=
\int_0^{x^a} b(1-t)^{b-1}dt=\operatorname{P}\{Y_{1,b}\le x^a\}
=\operatorname{P}\{Y^{1/a}_{1,b}\le x\}

One may introduce generalised Kumaraswamy distributions by considering random variables of the form Y^{1/\gamma}_{\alpha,\beta}, with \gamma>0 and where Y_{\alpha,\beta} denotes a Beta distributed random variable with parameters \alpha and \beta. The raw moments of this generalized Kumaraswamy distribution are given by:

m_n = \frac{\Gamma(\alpha+\beta)\Gamma(\alpha+n/\gamma)}{\Gamma(\alpha)\Gamma(\alpha+\beta+n/\gamma)}.

Note that we can reobtain the original moments setting \alpha=1, \beta=b and \gamma=a. However, in general the cumulative distribution function does not have a closed form solution.

Related distributions[edit]


A good example of the use of the Kumaraswamy distribution is the storage volume of a reservoir of capacity zmax whose upper bound is zmax and lower bound is 0 (Fletcher & Ponnambalam, 1996).


  • Kumaraswamy, P. (1980). "A generalized probability density function for double-bounded random processes". Journal of Hydrology 46 (1-2): 79–88. doi:10.1016/0022-1694(80)90036-0. 
  • Fletcher, S.G., and Ponnambalam, K. (1996). "Estimation of reservoir yield and storage distribution using moments analysis". Journal of Hydrology 182 (1-4): 259–275. doi:10.1016/0022-1694(95)02946-X. 
  • Jones, M.C. (2009). "Kumaraswamy's distribution: A beta-type distribution with some tractability advantages". Statistical Methodology 6 (1): 70–81. doi:10.1016/j.stamet.2008.04.001. 
  • Lemonte, A.J. (2011). "Improved point estimation for the Kumaraswamy distribution". Journal of Statistical Computation and Simulation 81 (12): 1971–1982. doi:10.1080/00949655.2010.511621.