QuantLib_GeneralStatistics

Langue: en

Autres versions - même langue

Version: 378482 (fedora - 01/12/10)

Section: 3 (Bibliothèques de fonctions)

Sommaire

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}