Package org.drip.spline.pchip

Class MonotoneConvexHaganWest

java.lang.Object

org.drip.function.definition.R1ToR1

org.drip.spline.pchip.MonotoneConvexHaganWest

public class MonotoneConvexHaganWest
extends R1ToR1

MonotoneConvexHaganWest implements the regime using the Hagan and West (2006) Estimator. It provides the following functionality:

• Create an instance of MonotoneConvexHaganWest
• Enforce the Positivity of the Inferred Response Values
• Create an Ameliorated Instance of the Current Instance
• Retrieve the Array of Predictor Ordinates
• Retrieve the Array of Response Values

Module	Product Core Module
Library	Fixed Income Analytics
Project	Basis Splines and Linear Compounders across a Broad Family of Spline Basis Functions
Package	Monotone Convex Themed PCHIP Splines

Author:

Lakshmi Krishnamurthy

Method Summary

Modifier and Type	Method	Description
static MonotoneConvexHaganWest	Create(double[] predictorOrdinateArray, double[] observationArray, boolean linearNodeInference)	Create an instance of MonotoneConvexHaganWest
boolean	enforcePositivity()	Enforce the Positivity of the Inferred Response Values
double	evaluate(double predictorOrdinate)	Evaluate for the given variate
MonotoneConvexHaganWest	generateAmelioratedInstance(double[] minimumLeftResponseArray, double[] maximumLeftResponseArray, double[] minimumRightResponseArray, double[] maximumRightResponseArray, boolean enforcePositivity)	Create an Ameliorated Instance of the Current Instance
double[]	predictorOrdinates()	Retrieve the Array of Predictor Ordinates
double[]	responseValues()	Retrieve the Array of Response Values

Methods inherited from class org.drip.function.definition.R1ToR1

antiDerivative, conditionNumber, derivative, differential, differential, integrate, maxima, maxima, minima, minima, poleResidue

Methods inherited from class java.lang.Object

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

Method Details

Create

public static final MonotoneConvexHaganWest Create(double[] predictorOrdinateArray,
double[] observationArray,
boolean linearNodeInference)

Create an instance of MonotoneConvexHaganWest

Parameters:

predictorOrdinateArray - Array of Predictor Ordinates

observationArray - Array of Observations

linearNodeInference - Apply Linear Node Inference from Observations

Returns:

Instance of MonotoneConvexHaganWest

evaluate

public double evaluate(double predictorOrdinate)
                throws java.lang.Exception

Description copied from class: R1ToR1

Evaluate for the given variate

Specified by:

evaluate in class R1ToR1

Parameters:

predictorOrdinate - Variate

Returns:

Returns the calculated value

Throws:

java.lang.Exception - Thrown if evaluation cannot be done

enforcePositivity

public boolean enforcePositivity()

Enforce the Positivity of the Inferred Response Values

Returns:

TRUE - Positivity Enforcement is successful

generateAmelioratedInstance

public MonotoneConvexHaganWest generateAmelioratedInstance(double[] minimumLeftResponseArray,
double[] maximumLeftResponseArray,
double[] minimumRightResponseArray,
double[] maximumRightResponseArray,
boolean enforcePositivity)

Create an Ameliorated Instance of the Current Instance

Parameters:

minimumLeftResponseArray - Response Left Floor

maximumLeftResponseArray - Response Left Ceiling

minimumRightResponseArray - Response Right Floor

maximumRightResponseArray - Response Right Ceiling

enforcePositivity - TRUE - Enforce Positivity

Returns:

The Ameliorated Version of the Current Instance

predictorOrdinates

public double[] predictorOrdinates()

Retrieve the Array of Predictor Ordinates

Returns:

The Array of Predictor Ordinates

responseValues

public double[] responseValues()

Retrieve the Array of Response Values

Returns:

The Array of Response Values