ClaytonCopulaFactory

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class ClaytonCopulaFactory(*args)

Clayton Copula factory.

We use the following estimator:

See also

DistributionFactory, ClaytonCopula

Methods

build(*args)	Build the distribution.
buildEstimator(*args)	Build the distribution and the parameter distribution.
getBootstrapSize()	Accessor to the bootstrap size.
getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getName()	Accessor to the object's name.
getShadowedId()	Accessor to the object's shadowed id.
getVisibility()	Accessor to the object's visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
setBootstrapSize(bootstrapSize)	Accessor to the bootstrap size.
setName(name)	Accessor to the object's name.
setShadowedId(id)	Accessor to the object's shadowed id.
setVisibility(visible)	Accessor to the object's visibility state.

buildAsClaytonCopula

__init__(*args)

build(*args)

Build the distribution.

Available usages:

build(sample)

build(param)

Parameters

sample2-d sequence of float

Sample from which the distribution parameters are estimated.

paramCollection of PointWithDescription

A vector of parameters of the distribution.

Returns

distDistribution

The built distribution.

buildEstimator(*args)

Build the distribution and the parameter distribution.

Parameters

sample2-d sequence of float

Sample from which the distribution parameters are estimated.

parametersDistributionParameters

Optional, the parametrization.

Returns

resDistDistributionFactoryResult

The results.

Notes

According to the way the native parameters of the distribution are estimated, the parameters distribution differs:

• Moments method: the asymptotic parameters distribution is normal and estimated by Bootstrap on the initial data;

• Maximum likelihood method with a regular model: the asymptotic parameters distribution is normal and its covariance matrix is the inverse Fisher information matrix;

• Other methods: the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting (see KernelSmoothing).

If another set of parameters is specified, the native parameters distribution is first estimated and the new distribution is determined from it:

• if the native parameters distribution is normal and the transformation regular at the estimated parameters values: the asymptotic parameters distribution is normal and its covariance matrix determined from the inverse Fisher information matrix of the native parameters and the transformation;

• in the other cases, the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting.

getBootstrapSize()

Accessor to the bootstrap size.

Returns

sizeinteger

Size of the bootstrap.

getClassName()

Accessor to the object’s name.

Returns

class_namestr

The object class name (object.__class__.__name__).

getId()

Accessor to the object’s id.

Returns

idint

Internal unique identifier.

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getShadowedId()

Accessor to the object’s shadowed id.

Returns

idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns

visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns

hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool

True if the name is not empty and not the default one.

setBootstrapSize(bootstrapSize)

Accessor to the bootstrap size.

Parameters

sizeinteger

Size of the bootstrap.

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters

idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters

visiblebool

Visibility flag.