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QuantLib_GeneralStatistics
Langue: en
Version: 378482 (fedora - 01/12/10)
Section: 3 (Bibliothèques de fonctions)
Sommaire
- NAME
- SYNOPSIS
- Detailed Description
- Member Function Documentation
- Real mean () constreturns the mean, defined as [
- ngle = ac{um w_i x_i}{um w_i}. ]
- Real variance () constreturns the variance, defined as [ igma^2 = ac{N}{N-1}
- . ]
- Real standardDeviation () constreturns the standard deviation $ igma $, defined as the square root of the variance.
- Real errorEstimate () constreturns the error estimate on the mean value, defined as $ \psilon = igma/qrt{N}. $
- Real skewness () constreturns the skewness, defined as [ ac{N^2}{(N-1)(N-2)} ac{
- }{igma^3}. ] The above evaluates to 0 for a Gaussian distribution.
- Real kurtosis () constreturns the excess kurtosis, defined as [ ac{N^2(N+1)}{(N-1)(N-2)(N-3)} ac{
- }{igma^4} - ac{3(N-1)^2}{(N-2)(N-3)}. ] The above evaluates to 0 for a Gaussian distribution.
- Real min () constreturns the minimum sample value
- Real max () constreturns the maximum sample value
- std::pair<Real,Size> expectationValue (const Func & f, const Predicate & inRange) constExpectation value of a function $ f $ on a given range $ mathcal{R} $, i.e., [ mathrm{E}
- range is passed as a boolean function returning true if the argument belongs to the range or false otherwise.
- Real percentile (Real y) const$ y $-th percentile, defined as the value $ t [ y = ac{um_{x_i < nge $ (0-1]. $
- Real topPercentile (Real y) const$ y $-th top percentile, defined as the value $ t [ y = ac{um_{x_i > nge $ (0-1]. $
- void add (Real value, Real weight = 1.0)
- Author
NAME
QuantLib::GeneralStatistics -Statistics tool.
SYNOPSIS
#include <ql/math/statistics/generalstatistics.hpp>
Public Types
typedef Real value_type
Public Member Functions
Inspectors
Size samples () const
number of samples collected
const std::vector< std::pair< Real, Real > > & data () const
collected data
Real weightSum () const
sum of data weights
Real mean () const
Real variance () const
Real standardDeviation () const
Real errorEstimate () const
Real skewness () const
Real kurtosis () const
Real min () const
Real max () const
template<class Func , class Predicate > std::pair< Real, Size > expectationValue (const Func &f, const Predicate &inRange) const
Real percentile (Real y) const
Real topPercentile (Real y) const
Modifiers
void add (Real value, Real weight=1.0)
adds a datum to the set, possibly with a weight
template<class DataIterator > void addSequence (DataIterator begin, DataIterator end)
adds a sequence of data to the set, with default weight
template<class DataIterator , class WeightIterator > void addSequence (DataIterator begin, DataIterator end, WeightIterator wbegin)
adds a sequence of data to the set, each with its weight
void reset ()
resets the data to a null set
void sort () const
sort the data set in increasing order
Detailed Description
Statistics tool.
This class accumulates a set of data and returns their statistics (e.g: mean, variance, skewness, kurtosis, error estimation, percentile, etc.) based on the empirical distribution (no gaussian assumption)
It doesn't suffer the numerical instability problem of IncrementalStatistics. The downside is that it stores all samples, thus increasing the memory requirements.
Member Function Documentation
Real mean () constreturns the mean, defined as [
ngle = ac{um w_i x_i}{um w_i}. ]
Real variance () constreturns the variance, defined as [ igma^2 = ac{N}{N-1}
. ]
Real standardDeviation () constreturns the standard deviation $ igma $, defined as the square root of the variance.
Real errorEstimate () constreturns the error estimate on the mean value, defined as $ \psilon = igma/qrt{N}. $
Real skewness () constreturns the skewness, defined as [ ac{N^2}{(N-1)(N-2)} ac{
}{igma^3}. ] The above evaluates to 0 for a Gaussian distribution.
Real kurtosis () constreturns the excess kurtosis, defined as [ ac{N^2(N+1)}{(N-1)(N-2)(N-3)} ac{
}{igma^4} - ac{3(N-1)^2}{(N-2)(N-3)}. ] The above evaluates to 0 for a Gaussian distribution.
Real min () constreturns the minimum sample value
Real max () constreturns the maximum sample value
std::pair<Real,Size> expectationValue (const Func & f, const Predicate & inRange) constExpectation value of a function $ f $ on a given range $ mathcal{R} $, i.e., [ mathrm{E}
range is passed as a boolean function returning true if the argument belongs to the range or false otherwise.
The function returns a pair made of the result and the number of observations in the given range.
Real percentile (Real y) const$ y $-th percentile, defined as the value $ t [ y = ac{um_{x_i < nge $ (0-1]. $
Real topPercentile (Real y) const$ y $-th top percentile, defined as the value $ t [ y = ac{um_{x_i > nge $ (0-1]. $
void add (Real value, Real weight = 1.0)
adds a datum to the set, possibly with a weight Precondition:
- weights must be positive or null
Author
Generated automatically by Doxygen for QuantLib from the source code.
- NAME
- SYNOPSIS
-
- Public Types
- Public Member Functions
- Detailed Description
- Member Function Documentation
-
- Real mean () constreturns the mean, defined as [
-
- ngle = ac{um w_i x_i}{um w_i}. ]
- Real variance () constreturns the variance, defined as [ igma^2 = ac{N}{N-1}
-
- . ]
- Real standardDeviation () constreturns the standard deviation $ igma $, defined as the square root of the variance.
- Real errorEstimate () constreturns the error estimate on the mean value, defined as $ \psilon = igma/qrt{N}. $
- Real skewness () constreturns the skewness, defined as [ ac{N^2}{(N-1)(N-2)} ac{
-
- }{igma^3}. ] The above evaluates to 0 for a Gaussian distribution.
- Real kurtosis () constreturns the excess kurtosis, defined as [ ac{N^2(N+1)}{(N-1)(N-2)(N-3)} ac{
-
- }{igma^4} - ac{3(N-1)^2}{(N-2)(N-3)}. ] The above evaluates to 0 for a Gaussian distribution.
- Real min () constreturns the minimum sample value
- Real max () constreturns the maximum sample value
- std::pair<Real,Size> expectationValue (const Func & f, const Predicate & inRange) constExpectation value of a function $ f $ on a given range $ mathcal{R} $, i.e., [ mathrm{E}
-
Contenus ©2006-2024 Benjamin Poulain
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