# Kurtosis

Kumaraswamy's double bounded distribution excess kurtosis.

The excess kurtosis for a Kumaraswamy's double bounded random variable with first shape parameter a and second shape parameter b is

where the raw moments of the distribution are given by

with B denoting the beta function.

## Usage

var kurtosis = require( '@stdlib/stats/base/dists/kumaraswamy/kurtosis' );


#### kurtosis( a, b )

Returns the excess kurtosis of a Kumaraswamy's double bounded distribution with first shape parameter a and second shape parameter b.

var v = kurtosis( 1.0, 1.0 );
// returns ~1.8

v = kurtosis( 4.0, 12.0 );
// returns ~2.704

v = kurtosis( 2.0, 8.0 );
// returns ~2.666


If provided NaN as any argument, the function returns NaN.

var v = kurtosis( NaN, 2.0 );
// returns NaN

v = kurtosis( 2.0, NaN );
// returns NaN


If provided a <= 0, the function returns NaN.

var y = kurtosis( -1.0, 0.5 );
// returns NaN

y = kurtosis( 0.0, 0.5 );
// returns NaN


If provided b <= 0, the function returns NaN.

var y = kurtosis( 0.5, -1.0 );
// returns NaN

y = kurtosis( 0.5, 0.0 );
// returns NaN


## Examples

var randu = require( '@stdlib/random/base/randu' );
var kurtosis = require( '@stdlib/stats/base/dists/kumaraswamy/kurtosis' );

var a;
var b;
var v;
var i;

for ( i = 0; i < 10; i++ ) {
a = randu() * 10.0;
b = randu() * 10.0;
v = kurtosis( a, b );
console.log( 'a: %d, b: %d, Kurt(X;a,b): %d', a.toFixed( 4 ), b.toFixed( 4 ), v.toFixed( 4 ) );
}