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SkewedGeneralizedError Class

Continuous Univariate Skewed Generalized Error Distribution (SGED). Implements the univariate Skewed Generalized Error Distribution. For details about this distribution, see Wikipedia - Generalized Error Distribution. It includes Laplace, Normal and Student-t distributions. This is the SkewedGeneralizedT distribution with q=Inf.
Inheritance Hierarchy
SystemObject
  Altaxo.Calc.DistributionsSkewedGeneralizedError

Namespace: Altaxo.Calc.Distributions
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
Syntax
C#
public class SkewedGeneralizedError : IContinuousDistribution, 
	IUnivariateDistribution, IDistribution

The SkewedGeneralizedError type exposes the following members.

Constructors
 NameDescription
Public methodSkewedGeneralizedError Initializes a new instance of the SkewedGeneralizedError class. This is a generalized error distribution with location=0.0, scale=1.0, skew=0.0 and p=2.0 (a standard normal distribution).
Public methodSkewedGeneralizedError(Double, Double, Double, Double) Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew and kurtosis parameters. Different parameterizations result in different distributions.
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Properties
 NameDescription
Public propertyEntropy 
Public propertyLocation Gets the location (μ) of the Skewed Generalized t-distribution.
Public propertyMaximum 
Public propertyMean 
Public propertyMedian 
Public propertyMinimum 
Public propertyMode 
Public propertyP Gets the parameter that controls the kurtosis of the distribution. Range: p > 0.
Public propertyRandomSource Gets or sets the random number generator which is used to draw random samples.
Public propertyScale Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
Public propertySkew Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
Public propertySkewness 
Public propertyStdDev 
Public propertyVariance 
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Methods
 NameDescription
Public methodStatic memberCDF 
Public methodCumulativeDistribution 
Public methodDensity 
Public methodDensityLn 
Public methodEqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
Protected methodFinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
Public methodGetHashCodeServes as the default hash function.
(Inherited from Object)
Public methodGetTypeGets the Type of the current instance.
(Inherited from Object)
Public methodStatic memberInvCDF 
Public methodInverseCumulativeDistribution 
Public methodStatic memberIsValidParameterSet Tests whether the provided values are valid parameters for this distribution.
Protected methodMemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
Public methodStatic memberPDF 
Public methodStatic memberPDFLn 
Public methodSample 
Public methodStatic memberSample(Double, Double, Double, Double) Generates a sample from the Skew Generalized Error distribution.
Public methodStatic memberSample(Random, Double, Double, Double, Double) Generates a sample from the Skew Generalized Error distribution.
Public methodSamples 
Public methodSamples(Double) 
Public methodStatic memberSamples(Double, Double, Double, Double) Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
Public methodStatic memberSamples(Double, Double, Double, Double, Double) Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
Public methodStatic memberSamples(Random, Double, Double, Double, Double) Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
Public methodStatic memberSamples(Random, Double, Double, Double, Double, Double) Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
Public methodToString A string representation of the distribution.
(Overrides ObjectToString)
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Remarks

This implementation is based on the R package dsgt and corresponding viginette, see https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf. Compared to that implementation, the options for mean adjustment and variance adjustment are always true. The location (μ) is the mean of the distribution. The scale (σ) squared is the variance of the distribution.

The distribution will use the Random by default. Users can get/set the random number generator by using the RandomSource property.

The statistics classes will check all the incoming parameters whether they are in the allowed range.

See Also