edu.ucla.stat.SOCR.distributions
Class ErrorDistribution

java.lang.Object
  edu.ucla.stat.SOCR.core.SOCRValueSettable
      edu.ucla.stat.SOCR.core.Distribution
          edu.ucla.stat.SOCR.distributions.ErrorDistribution

All Implemented Interfaces:

IValueSettable, Pluginable, java.util.Observer

public class ErrorDistribution
extends Distribution

A Java implementation of the Error distribution with specified Location, Scale and Shape parameters http://en.wikipedia.org/wiki/Exponential_power_distribution.

Field Summary

Fields inherited from class edu.ucla.stat.SOCR.core.Distribution

applet, CONTINUOUS, DISCRETE, MAXMGFXVAL, MAXMGFYVAL, MINMGFXVAL, MIXED, name

Constructor Summary

ErrorDistribution()
Default constructor: creates an Error distribution with location, scale and shape parameters equal to 1.

ErrorDistribution(double[] distData)
Constructor: Creates a new Error distribution from a series of observations by parameter estimation.

ErrorDistribution(double a, double b, double c)
General constructor: creates an Error distribution with location(a), scale(b) and shape(c) parameters.

ErrorDistribution(float[] distData)
Constructor: Creates a new Error distribution from a series of observations by parameter estimation.

Method Summary

double	getDensity(double x) Define the Error getDensity function
double	getLocation() Get the Location paramter
double	getMaxDensity() Compute the maximum getDensity
double	getMean() Compute the mean in closed form
double	getMode() Compute the Mode in closed form
java.lang.String	getOnlineDescription() This method returns an online description of this distribution.
double	getScale() Get the scale paramter
double	getSD() Compute the variance in closed form
double	getShape() Get the shape parameter
double	getVariance() Compute the variance in closed form
void	initialize() Class initialization.
void	paramEstimate(double[] distData) Overwrites the method in distribution for estimating parameters By assuming that the shape parameter is known, the location and scale parameters could be easily obtained by using the maximum likelihood estimation method.
void	setLocation(double a) Sets the Location parameter
void	setParameters(double a, double b, double c) Set the parameters, compute the normalizing constant NormalizingConst, and specifies the interval and partition
void	setScale(double b) Sets the Scale parameter
void	setShape(double c) Sets the Shape parameter
void	valueChanged()

Methods inherited from class edu.ucla.stat.SOCR.core.Distribution

addObserver, betaCDF, comb, factorial, findGFRoot, findRoot, gamma, gammaCDF, getCDF, getDisplayPane, getDomain, getFailureRate, getGFDerivative, getGFSecondDerivative, getInstance, getLocalHelp, getMean, getMedian, getMGF, getMgfDomain, getName, getPGF, getPGFDomain, getQuantile, getSampleMoment, getSOCRDistributionFunctors, getSOCRDistributions, getType, getVariance, inverseCDF, logGamma, perm, sampleMean, sampleVar, setApplet, setDomain, setDomain, setMGFDomain, setMGFDomain, setMGFParameters, setMGFParameters, setMGFParameters, setMGFParameters, setParameters, setPGFDomain, setPGFDomain, setPGFParameters, setPGFParameters, setPGFParameters, setPGFParameters, simulate, update, valueChanged

Methods inherited from class edu.ucla.stat.SOCR.core.SOCRValueSettable

createComponentSetter, createValueSetter, createValueSetter, createValueSetter, createValueSetter, getComponentSetter, getComponentSetters, getValueSetter, getValueSetters

Methods inherited from class java.lang.Object

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

Constructor Detail

ErrorDistribution

public ErrorDistribution()

Default constructor: creates an Error distribution with location, scale and shape parameters equal to 1.

ErrorDistribution

public ErrorDistribution(double a,
                         double b,
                         double c)

General constructor: creates an Error distribution with location(a), scale(b) and shape(c) parameters.

ErrorDistribution

public ErrorDistribution(double[] distData)

Constructor: Creates a new Error distribution from a series of observations by parameter estimation.

ErrorDistribution

public ErrorDistribution(float[] distData)

Constructor: Creates a new Error distribution from a series of observations by parameter estimation.

Method Detail

initialize

public void initialize()

Class initialization.

Overrides:

initialize in class Distribution

valueChanged

public void valueChanged()

Overrides:

valueChanged in class Distribution

setParameters

public void setParameters(double a,
                          double b,
                          double c)

Set the parameters, compute the normalizing constant NormalizingConst, and specifies the interval and partition

setLocation

public void setLocation(double a)

Sets the Location parameter

setScale

public void setScale(double b)

Sets the Scale parameter

setShape

public void setShape(double c)

Sets the Shape parameter

getLocation

public double getLocation()

Get the Location paramter

getScale

public double getScale()

Get the scale paramter

getShape

public double getShape()

Get the shape parameter

getDensity

public double getDensity(double x)

Define the Error getDensity function

Specified by:

getDensity in class Distribution

getMaxDensity

public double getMaxDensity()

Compute the maximum getDensity

Overrides:

getMaxDensity in class Distribution

getMean

public double getMean()

Compute the mean in closed form

Overrides:

getMean in class Distribution

getMode

public double getMode()

Compute the Mode in closed form

paramEstimate

public void paramEstimate(double[] distData)

Overwrites the method in distribution for estimating parameters By assuming that the shape parameter is known, the location and scale parameters could be easily obtained by using the maximum likelihood estimation method. The estimate of the shape parameter p is an open problem, so far. See this paper for an idea of how to implement a numerical scheme for estimation of the Shape parameter: http://www.jstatsoft.org/v12/i04/

Overrides:

paramEstimate in class Distribution

getVariance

public double getVariance()

Compute the variance in closed form

Overrides:

getVariance in class Distribution

getSD

public double getSD()

Compute the variance in closed form

Overrides:

getSD in class Distribution

getOnlineDescription

public java.lang.String getOnlineDescription()

This method returns an online description of this distribution.

Overrides:

getOnlineDescription in class Distribution