org.drip.spline.pchip

Class MonotoneConvexHaganWest

java.lang.Object

org.drip.function.definition.R1ToR1

org.drip.spline.pchip.MonotoneConvexHaganWest

public class MonotoneConvexHaganWest
extends R1ToR1

This class implements the regime using the Hagan and West (2006) Estimator. It provides the following functionality: - Static Method to Create an instance of MonotoneConvexHaganWest. - Ensure that the estimated regime is monotone an convex. - If need be, enforce positivity and/or apply amelioration. - Apply segment-by-segment range bounds as needed. - Retrieve predictor ordinates/response values.

Author:

Lakshmi Krishnamurthy

Method Summary

Modifier and Type	Method and Description
static MonotoneConvexHaganWest	Create(double[] adblPredictorOrdinate, double[] adblObservation, boolean bLinearNodeInference) Create an instance of MonotoneConvexHaganWest
boolean	enforcePositivity() Enforce the Positivity of the Inferred Response Values
double	evaluate(double dblPredictorOrdinate) Evaluate for the given variate
MonotoneConvexHaganWest	generateAmelioratedInstance(double[] adblResponseLeftMin, double[] adblResponseLeftMax, double[] adblResponseRightMin, double[] adblResponseRightMax, boolean bEnforcePositivity) 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

derivative, differential, differential, integrate, maxima, maxima, minima, minima

Methods inherited from class java.lang.Object

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

Method Detail

Create

public static final MonotoneConvexHaganWest Create(double[] adblPredictorOrdinate,
                                                   double[] adblObservation,
                                                   boolean bLinearNodeInference)

Create an instance of MonotoneConvexHaganWest

Parameters:

adblPredictorOrdinate - Array of Predictor Ordinates

adblObservation - Array of Observations

bLinearNodeInference - Apply Linear Node Inference from Observations

Returns:

Instance of MonotoneConvexHaganWest

evaluate

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

Description copied from class: R1ToR1

Evaluate for the given variate

Specified by:

evaluate in class R1ToR1

Parameters:

dblPredictorOrdinate - 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[] adblResponseLeftMin,
                                                           double[] adblResponseLeftMax,
                                                           double[] adblResponseRightMin,
                                                           double[] adblResponseRightMax,
                                                           boolean bEnforcePositivity)

Create an Ameliorated Instance of the Current Instance

Parameters:

adblResponseLeftMin - Response Left Floor

adblResponseLeftMax - Response Left Ceiling

adblResponseRightMin - Response Right Floor

adblResponseRightMax - Response Right Ceiling

bEnforcePositivity - 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