com.numericalmethod.suanshu.stats.test.distribution.normality
Class JarqueBeraDistribution

java.lang.Object
  com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
      com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBeraDistribution

All Implemented Interfaces:

UnivariateDistribution

public class JarqueBeraDistribution
extends EmpiricalDistribution

Jarque–Bera distribution is the distribution of the Jarque–Bera statistics, which measures the departure from normality.

The statistics is

       n
 JB = --- (S2) + 0.25 * K2))
       6
 

S is the skewness; K is the kurtosis.

See Also:

Wikipedia: Jarque–Bera test

Field Summary

int	N the number of observations in a sample
int	nSim the number of Monte Carlo simulation paths

Constructor Summary

JarqueBeraDistribution(int N, int nSim)
Construct a Jarque–Bera distribution using Monte Carlo simulation.

JarqueBeraDistribution(int N, int nSim, StandardGaussian rnorm)
Construct a Jarque–Bera distribution using Monte Carlo simulation.

Method Summary

Methods inherited from class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution

cdf, density, entropy, kurtosis, mean, median, moment, quantile, skew, variance

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

N

public final int N

the number of observations in a sample

nSim

public final int nSim

the number of Monte Carlo simulation paths

Constructor Detail

JarqueBeraDistribution

public JarqueBeraDistribution(int N,
                              int nSim,
                              StandardGaussian rnorm)

Construct a Jarque–Bera distribution using Monte Carlo simulation.

Parameters:

N - number of observations in a sample

nSim - number of simulations

rnorm - a Gaussian random number generator

JarqueBeraDistribution

public JarqueBeraDistribution(int N,
                              int nSim)

Construct a Jarque–Bera distribution using Monte Carlo simulation.

Parameters:

N - number of observations in a sample

nSim - number of simulations