Class ConvergenceStatistics
Constructors
constructor
Defined in ql/math/statistics/convergencestatistics.ts:28
Parameters
T: any
Default value U: any = new DoublingConvergenceSteps()
Properties
T
T: any
Defined in ql/math/statistics/convergencestatistics.ts:183
U
U: any
Defined in ql/math/statistics/convergencestatistics.ts:185
Private _nextSampleSize
Defined in ql/math/statistics/convergencestatistics.ts:190
Private _samplingRule _samplingRule: any
Defined in ql/math/statistics/convergencestatistics.ts:189
Private _table
Defined in ql/math/statistics/convergencestatistics.ts:187
Methods
add
Defined in ql/math/statistics/convergencestatistics.ts:37
Parameters
Default value weight: Real = 1
Returns void
addSequence
addSequence( values: Real [] , weights?: Real [] ) : void
Defined in ql/math/statistics/convergencestatistics.ts:46
Parameters
values: Real []
Default value weights: Real [] = []
Returns void
convergenceTable
Defined in ql/math/statistics/convergencestatistics.ts:64
Returns Array < [ Size , Real ] >
downsideSamples
Defined in ql/math/statistics/convergencestatistics.ts:134
downsideWeightSum
downsideWeightSum( ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:138
errorEstimate
Defined in ql/math/statistics/convergencestatistics.ts:100
expectationValue1
Defined in ql/math/statistics/convergencestatistics.ts:104
Parameters
expectationValue2
Defined in ql/math/statistics/convergencestatistics.ts:108
Parameters
gaussianAverageShortfall
gaussianAverageShortfall( target: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:178
Parameters
gaussianDownsideDeviation
gaussianDownsideDeviation( ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:146
gaussianDownsideVariance
gaussianDownsideVariance( ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:142
gaussianExpectedShortfall
gaussianExpectedShortfall( percentile: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:170
Parameters
gaussianPercentile
gaussianPercentile( percentile: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:154
Parameters
gaussianPotentialUpside
gaussianPotentialUpside( percentile: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:162
Parameters
gaussianRegret
Defined in ql/math/statistics/convergencestatistics.ts:150
Parameters
gaussianShortfall
Defined in ql/math/statistics/convergencestatistics.ts:174
Parameters
gaussianTopPercentile
gaussianTopPercentile( percentile: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:158
Parameters
gaussianValueAtRisk
gaussianValueAtRisk( percentile: Real ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:166
Parameters
kurtosis
Defined in ql/math/statistics/convergencestatistics.ts:122
max
Defined in ql/math/statistics/convergencestatistics.ts:88
mean
Defined in ql/math/statistics/convergencestatistics.ts:92
min
Defined in ql/math/statistics/convergencestatistics.ts:84
percentile
Defined in ql/math/statistics/convergencestatistics.ts:126
Parameters
reset
Defined in ql/math/statistics/convergencestatistics.ts:58
Returns void
samples1
Defined in ql/math/statistics/convergencestatistics.ts:68
samples2
Defined in ql/math/statistics/convergencestatistics.ts:72
Parameters
skewness
Defined in ql/math/statistics/convergencestatistics.ts:118
standardDeviation
standardDeviation( ) : Real
Defined in ql/math/statistics/convergencestatistics.ts:114
topPercentile
Defined in ql/math/statistics/convergencestatistics.ts:130
Parameters
variance
Defined in ql/math/statistics/convergencestatistics.ts:96
weightSum1
Defined in ql/math/statistics/convergencestatistics.ts:76
weightSum2
Defined in ql/math/statistics/convergencestatistics.ts:80
Parameters
Legend
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Inherited constructor Inherited property Inherited method Inherited accessor
Protected property Protected method Protected accessor
Private property Private method Private accessor
Static property Static method
statistics class with convergence table
This class decorates another statistics class adding a convergence table calculation. The table tracks the convergence of the mean.
It is possible to specify the number of samples at which the mean should be stored by mean of the second template parameter; the default is to store $ 2^{n-1} $ samples at the $ n $-th step. Any passed class must implement the following interface:
Size initialSamples() const; Size nextSamples(Size currentSamples) const;as well as a copy constructor.
test results are tested against known good values.