org.drip.sequence.functional

Class EfronSteinMetrics

java.lang.Object

org.drip.sequence.functional.EfronSteinMetrics

Direct Known Subclasses:

EmpiricalPenaltySupremumMetrics

public class EfronSteinMetrics
extends java.lang.Object

EfronSteinMetrics contains the Variance-based non-exponential Sample Distribution/Bounding Metrics and Agnostic Bounds related to the Functional Transformation of the specified Sequence.

Author:

Lakshmi Krishnamurthy

Constructor Summary

Constructors

Constructor and Description
EfronSteinMetrics(MultivariateRandom func, SingleSequenceAgnosticMetrics[] aSSAM) EfronSteinMetrics Constructor

Method Summary

Modifier and Type	Method and Description
double	boundedVarianceUpperBound() Compute the Multivariate Variance Upper Bound using the Bounded Differences Support
double	efronSteinSteeleBound(SingleSequenceAgnosticMetrics[] aSSAMGhost) Compute the Efron-Stein-Steele Variance Upper Bound using the Ghost Variables
RdToR1	function() Retrieve the Multivariate Objective Function
double	ghostVarianceUpperBound(SingleSequenceAgnosticMetrics[] aSSAMGhost) Compute the Variance Upper Bound using the Ghost Variables
SingleSequenceAgnosticMetrics[]	ghostVariateVarianceMetrics(SingleSequenceAgnosticMetrics[] aSSAMGhost) Compute the Function Sequence Agnostic Metrics associated with the Variance of each Variate Using the Supplied Ghost Variate Sequence
double	martingaleVarianceUpperBound() Compute the Multivariate Variance Upper Bound using the Martingale Differences Method
SingleSequenceAgnosticMetrics[]	pivotedDifferenceSequenceMetrics(MultivariateRandom funcPivot) Compute the Function Sequence Agnostic Metrics associated with each Variate around the Pivot Point provided by the Pivot Function
double	pivotVarianceUpperBound(MultivariateRandom funcPivot) Compute the Function Variance Upper Bound using the supplied Multivariate Pivoting Function
double	separableVarianceUpperBound() Compute the Multivariate Variance Upper Bound using the Separable Variance Bound
SingleSequenceAgnosticMetrics[]	sequenceMetrics() Retrieve the Array of the Single Sequence Agnostic Metrics
SingleSequenceAgnosticMetrics[]	symmetrizedDifferenceSequenceMetrics(SingleSequenceAgnosticMetrics[] aSSAMGhost) Compute the Function Sequence Agnostic Metrics associated with each Variate using the specified Ghost Symmetric Variable Copy
SingleSequenceAgnosticMetrics[]	variateFunctionVarianceMetrics() Compute the Function Sequence Agnostic Metrics associated with the Variance of each Variate
double[][]	variateSequence(SingleSequenceAgnosticMetrics[] aSSAM) Extract the Full Variate Array Sequence

Methods inherited from class java.lang.Object

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

Constructor Detail

EfronSteinMetrics

public EfronSteinMetrics(MultivariateRandom func,
                         SingleSequenceAgnosticMetrics[] aSSAM)
                  throws java.lang.Exception

EfronSteinMetrics Constructor

Parameters:

func - Multivariate Objective Function

aSSAM - Array of the individual Single Sequence Metrics

Throws:

java.lang.Exception - Thrown if the Inputs are Invalid

Method Detail

function

public RdToR1 function()

Retrieve the Multivariate Objective Function

Returns:

The Multivariate Objective Function Instance

sequenceMetrics

public SingleSequenceAgnosticMetrics[] sequenceMetrics()

Retrieve the Array of the Single Sequence Agnostic Metrics

Returns:

The Array of the Single Sequence Agnostic Metrics

variateSequence

public double[][] variateSequence(SingleSequenceAgnosticMetrics[] aSSAM)

Extract the Full Variate Array Sequence

Parameters:

aSSAM - Array of the individual Single Sequence Metrics

Returns:

The Full Variate Array Sequence

variateFunctionVarianceMetrics

public SingleSequenceAgnosticMetrics[] variateFunctionVarianceMetrics()

Compute the Function Sequence Agnostic Metrics associated with the Variance of each Variate

Returns:

The Array of the Associated Sequence Metrics

ghostVariateVarianceMetrics

public SingleSequenceAgnosticMetrics[] ghostVariateVarianceMetrics(SingleSequenceAgnosticMetrics[] aSSAMGhost)

Compute the Function Sequence Agnostic Metrics associated with the Variance of each Variate Using the Supplied Ghost Variate Sequence

Parameters:

aSSAMGhost - Array of the Ghost Single Sequence Metrics

Returns:

The Array of the Associated Sequence Metrics

symmetrizedDifferenceSequenceMetrics

public SingleSequenceAgnosticMetrics[] symmetrizedDifferenceSequenceMetrics(SingleSequenceAgnosticMetrics[] aSSAMGhost)

Compute the Function Sequence Agnostic Metrics associated with each Variate using the specified Ghost Symmetric Variable Copy

Parameters:

aSSAMGhost - Array of the Ghost Single Sequence Metrics

Returns:

The Array of the Associated Sequence Metrics

pivotedDifferenceSequenceMetrics

public SingleSequenceAgnosticMetrics[] pivotedDifferenceSequenceMetrics(MultivariateRandom funcPivot)

Compute the Function Sequence Agnostic Metrics associated with each Variate around the Pivot Point provided by the Pivot Function

Parameters:

funcPivot - The Pivot Function

Returns:

The Array of the Associated Sequence Metrics

martingaleVarianceUpperBound

public double martingaleVarianceUpperBound()
                                    throws java.lang.Exception

Compute the Multivariate Variance Upper Bound using the Martingale Differences Method

Returns:

The Multivariate Variance Upper Bound using the Martingale Differences Method

Throws:

java.lang.Exception - Thrown if the Upper Bound cannot be calculated

ghostVarianceUpperBound

public double ghostVarianceUpperBound(SingleSequenceAgnosticMetrics[] aSSAMGhost)
                               throws java.lang.Exception

Compute the Variance Upper Bound using the Ghost Variables

Parameters:

aSSAMGhost - Array of the Ghost Single Sequence Metrics

Returns:

The Variance Upper Bound using the Ghost Variables

Throws:

java.lang.Exception - Thrown if the Upper Bound cannot be calculated

efronSteinSteeleBound

public double efronSteinSteeleBound(SingleSequenceAgnosticMetrics[] aSSAMGhost)
                             throws java.lang.Exception

Compute the Efron-Stein-Steele Variance Upper Bound using the Ghost Variables

Parameters:

aSSAMGhost - Array of the Ghost Single Sequence Metrics

Returns:

The Efron-Stein-Steele Variance Upper Bound using the Ghost Variables

Throws:

java.lang.Exception - Thrown if the Upper Bound cannot be calculated

pivotVarianceUpperBound

public double pivotVarianceUpperBound(MultivariateRandom funcPivot)
                               throws java.lang.Exception

Compute the Function Variance Upper Bound using the supplied Multivariate Pivoting Function

Parameters:

funcPivot - The Custom Multivariate Pivoting Function

Returns:

The Function Variance Upper Bound using the supplied Multivariate Pivot Function

Throws:

java.lang.Exception - Thrown if the Variance Upper Bound cannot be calculated

boundedVarianceUpperBound

public double boundedVarianceUpperBound()
                                 throws java.lang.Exception

Compute the Multivariate Variance Upper Bound using the Bounded Differences Support

Returns:

The Multivariate Variance Upper Bound using the Bounded Differences Support

Throws:

java.lang.Exception - Thrown if the Upper Bound cannot be calculated

separableVarianceUpperBound

public double separableVarianceUpperBound()
                                   throws java.lang.Exception

Compute the Multivariate Variance Upper Bound using the Separable Variance Bound

Returns:

The Multivariate Variance Upper Bound using the Separable Variance Bound

Throws:

java.lang.Exception - Thrown if the Upper Bound cannot be calculated