Package org.drip.sequence.metrics

Class UnitSequenceAgnosticMetrics

java.lang.Object

org.drip.sequence.metrics.SingleSequenceAgnosticMetrics

org.drip.sequence.metrics.BoundedSequenceAgnosticMetrics

org.drip.sequence.metrics.UnitSequenceAgnosticMetrics

public class UnitSequenceAgnosticMetrics
extends BoundedSequenceAgnosticMetrics

UnitSequenceAgnosticMetrics contains the Sample Distribution Metrics and Agnostic Bounds related to the specified Bounded [0, 1] Sequence.

• Module = Numerical Core Module
• Library = Statistical Learning Library
• Project = Sequence
• Package = Metrics

Author:

Lakshmi Krishnamurthy

Constructor Summary

Constructors

Constructor	Description
UnitSequenceAgnosticMetrics(double[] adblSequence, double dblPopulationMean)	UnitSequenceAgnosticMetrics Constructor

Method Summary

Modifier and Type	Method	Description
double	chernoffBinomialUpperBound(double dblLevel)	Compute the Chernoff Binomial Upper Bound
double	chernoffPoissonUpperBound(double dblLevel)	Compute the Chernoff-Poisson Binomial Upper Bound
PivotedDepartureBounds	karpHagerupRubBounds(double dblLevel)	Compute the Karp/Hagerup/Rub Pivot Departure Bounds outlined below: - Karp, R.
double	populationMean()	Retrieve the Mean of the Underlying Distribution

Methods inherited from class org.drip.sequence.metrics.BoundedSequenceAgnosticMetrics

bennettAverageBounds, bernsteinAverageBounds, chernoffHoeffdingAverageBounds, support

Methods inherited from class org.drip.sequence.metrics.SingleSequenceAgnosticMetrics

centralMomentBound, chebyshevAssociationBound, chebyshevBound, chebyshevCantelliBound, empiricalAnchorMoment, empiricalCentralMoment, empiricalExpectation, empiricalRawMoment, empiricalVariance, functionSequenceMetrics, isPositive, markovUpperProbabilityBound, populationDistribution, populationVariance, sequence, weakLawAverageBounds

Methods inherited from class java.lang.Object

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

Constructor Details

UnitSequenceAgnosticMetrics

public UnitSequenceAgnosticMetrics(double[] adblSequence,
double dblPopulationMean)
                            throws java.lang.Exception

UnitSequenceAgnosticMetrics Constructor

Parameters:

adblSequence - The Random Sequence

dblPopulationMean - The Mean of the Underlying Distribution

Throws:

java.lang.Exception - Thrown if UnitSequenceAgnosticMetrics cannot be constructed

Method Details

populationMean

public double populationMean()

Retrieve the Mean of the Underlying Distribution

Overrides:

populationMean in class SingleSequenceAgnosticMetrics

Returns:

The Mean of the Underlying Distribution

chernoffBinomialUpperBound

public double chernoffBinomialUpperBound(double dblLevel)
                                  throws java.lang.Exception

Compute the Chernoff Binomial Upper Bound

Parameters:

dblLevel - The Level at which the Bound is sought

Returns:

The Chernoff Binomial Upper Bound

Throws:

java.lang.Exception - Thrown if the Chernoff Binomial Upper Bound cannot be computed

chernoffPoissonUpperBound

public double chernoffPoissonUpperBound(double dblLevel)
                                 throws java.lang.Exception

Compute the Chernoff-Poisson Binomial Upper Bound

Parameters:

dblLevel - The Level at which the Bound is sought

Returns:

The Chernoff-Poisson Binomial Upper Bound

Throws:

java.lang.Exception - Thrown if the Chernoff-Poisson Binomial Upper Bound cannot be computed

karpHagerupRubBounds

public PivotedDepartureBounds karpHagerupRubBounds(double dblLevel)

Compute the Karp/Hagerup/Rub Pivot Departure Bounds outlined below: - Karp, R. M. (1988): Probabilistic Analysis of Algorithms, University of California, Berkeley. - Hagerup, T., and C. Rub (1990): A Guided Tour of Chernoff Bounds, Information Processing Letters, 33:305-308.

Parameters:

dblLevel - The Level at which the Bound is sought

Returns:

The Karp/Hagerup/Rub Pivot Departure Bounds