Class QuantileCalculationMethod
 java.lang.Object

 com.opengamma.strata.math.impl.statistics.descriptive.QuantileCalculationMethod

 Direct Known Subclasses:
DiscreteQuantileMethod
,ExponentiallyWeightedInterpolationQuantileMethod
,InterpolationQuantileMethod
public abstract class QuantileCalculationMethod extends Object
Abstract method to estimate quantiles and expected shortfalls from sample observations.


Constructor Summary
Constructors Constructor Description QuantileCalculationMethod()

Method Summary
All Methods Instance Methods Abstract Methods Concrete Methods Modifier and Type Method Description protected double
checkIndex(double index, int size, boolean isExtrapolated)
Check the index is within the sample data range.protected abstract QuantileResult
expectedShortfall(double level, DoubleArray sample)
Computed the expected shortfall.double
expectedShortfallFromSorted(double level, DoubleArray sortedSample)
Compute the expected shortfall.double
expectedShortfallFromUnsorted(double level, DoubleArray sample)
Compute the expected shortfall.QuantileResult
expectedShortfallResultFromUnsorted(double level, DoubleArray sample)
Compute the expected shortfall.protected abstract QuantileResult
quantile(double level, DoubleArray sample, boolean isExtrapolated)
Computed the quantile.double
quantileFromSorted(double level, DoubleArray sortedSample)
Compute the quantile estimation.double
quantileFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.QuantileResult
quantileResultFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.QuantileResult
quantileResultWithExtrapolationFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.double
quantileWithExtrapolationFromSorted(double level, DoubleArray sortedSample)
Compute the quantile estimation.double
quantileWithExtrapolationFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.



Method Detail

quantileResultFromUnsorted
public QuantileResult quantileResultFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, the quantile estimation with the level 99% corresponds to the smallest 99% observations and 1% of the observation are above that level.
If index value computed from the level is outside of the sample data range,
IllegalArgumentException
is thrown.The sample observations are supposed to be unsorted.
The quantile result produced contains the quantile value, the indices of the data points used to compute it as well as the weights assigned to each point in the computation. The indices are based on the original, unsorted array. Additionally, the indices start from 0 and so do not need to be shifted to account for java indexing, when using them to reference the data points in the quantile calculation.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 the quantile estimation

quantileResultWithExtrapolationFromUnsorted
public QuantileResult quantileResultWithExtrapolationFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, the quantile estimation with the level 99% corresponds to the smallest 99% observations and 1% of the observation are above that level.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., quantile is computed with flat extrapolation.
The sample observations are supposed to be unsorted.
The quantile result produced contains the quantile value, the indices of the data points used to compute it as well as the weights assigned to each point in the computation. The indices are based on the original, unsorted array. Additionally, the indices start from 0 and so do not need to be shifted to account for java indexing, when using them to reference the data points in the quantile calculation.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 the quantile estimation

quantileFromSorted
public double quantileFromSorted(double level, DoubleArray sortedSample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the quantile estimation with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range,
IllegalArgumentException
is thrown.The sample observations are sorted from the smallest to the largest.
 Parameters:
level
 the quantile levelsortedSample
 the sample observations Returns:
 the quantile estimation

quantileFromUnsorted
public double quantileFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the quantile estimation with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range,
IllegalArgumentException
is thrown.The sample observations are supposed to be unsorted, the first step is to sort the data.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 The quantile estimation

quantileWithExtrapolationFromSorted
public double quantileWithExtrapolationFromSorted(double level, DoubleArray sortedSample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the quantile estimation with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., quantile is computed with flat extrapolation.
The sample observations are sorted from the smallest to the largest.
 Parameters:
level
 the quantile levelsortedSample
 the sample observations Returns:
 the quantile estimation

quantileWithExtrapolationFromUnsorted
public double quantileWithExtrapolationFromUnsorted(double level, DoubleArray sample)
Compute the quantile estimation.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the quantile estimation with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., quantile is computed with flat extrapolation.
The sample observations are supposed to be unsorted, the first step is to sort the data.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 The quantile estimation

expectedShortfallResultFromUnsorted
public QuantileResult expectedShortfallResultFromUnsorted(double level, DoubleArray sample)
Compute the expected shortfall.The shortfall level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, the expected shortfall with the level 99% corresponds to the average of the smallest 99% of the observations.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., expected short fall is computed with flat extrapolation. Thus this is coherent to
quantileWithExtrapolationFromUnsorted(double, DoubleArray)
.The sample observations are supposed to be unsorted.
The quantile result produced contains the expected shortfall value, the indices of the data points used to compute it as well as the weights assigned to each point in the computation. The indices are based on the original, unsorted array. Additionally, the indices start from 0 and so do not need to be shifted to account for java indexing, when using them to reference the data points in the quantile calculation.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 the quantile estimation

expectedShortfallFromSorted
public double expectedShortfallFromSorted(double level, DoubleArray sortedSample)
Compute the expected shortfall.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the expected shortfall with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., expected short fall is computed with flat extrapolation. Thus this is coherent to
quantileWithExtrapolationFromSorted(double, DoubleArray)
.The sample observations are sorted from the smallest to the largest.
 Parameters:
level
 the quantile levelsortedSample
 the sample observations Returns:
 the quantile estimation

expectedShortfallFromUnsorted
public double expectedShortfallFromUnsorted(double level, DoubleArray sample)
Compute the expected shortfall.The quantile level is in decimal, i.e. 99% = 0.99 and 0 < level < 1 should be satisfied. This is measured from the bottom, that is, Thus the expected shortfall with the level 99% corresponds to the smallest 99% observations.
If index value computed from the level is outside of the sample data range, the nearest data point is used, i.e., expected short fall is computed with flat extrapolation. Thus this is coherent to
quantileWithExtrapolationFromUnsorted(double, DoubleArray)
.The sample observations are supposed to be unsorted, the first step is to sort the data.
 Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 The expected shortfall estimation

quantile
protected abstract QuantileResult quantile(double level, DoubleArray sample, boolean isExtrapolated)
Computed the quantile.This protected method should be implemented in subclasses.
 Parameters:
level
 the quantile levelsample
 the sample observationsisExtrapolated
 extrapolated if true, not extrapolated otherwise. Returns:
 the quantile

expectedShortfall
protected abstract QuantileResult expectedShortfall(double level, DoubleArray sample)
Computed the expected shortfall.This protected method should be implemented in subclasses and coherent to
quantile(double, DoubleArray, boolean)
. Parameters:
level
 the quantile levelsample
 the sample observations Returns:
 the expected shortfall

checkIndex
protected double checkIndex(double index, int size, boolean isExtrapolated)
Check the index is within the sample data range.If the index is outside the data range, the nearest data point is used in case of
isExtrapolated == true
or an exception is thrown in case ofisExtrapolated == false
. Parameters:
index
 the indexsize
 the sample sizeisExtrapolated
 extrapolated if true, not extrapolated otherwise Returns:
 the index

