QuantLib_GeneralStatistics man page

typedef Real value_type

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 reserve (Size n) const
informs the internal storage of a planned increase in size
void sort () const
sort the data set in increasing order

returns the mean, defined as [ langle x rangle = ac{sum w_i x_i}{sum w_i}. ]

returns the variance, defined as [ sigma^2 = ac{N}{N-1} leftlangle left( x-langle x rangle right)^2 rightrangle. ]

returns the standard deviation $ sigma $, defined as the square root of the variance.

returns the error estimate on the mean value, defined as $ \psilon = sigma/sqrt{N}. $

returns the skewness, defined as [ ac{N^2}{(N-1)(N-2)} ac{leftlangle left( x-langle x rangle right)^3 rightrangle}{sigma^3}. ] The above evaluates to 0 for a Gaussian distribution.

returns the excess kurtosis, defined as [ ac{N^2(N+1)}{(N-1)(N-2)(N-3)} ac{leftlangle left(x-langle x rangle right)^4 rightrangle}{sigma^4} - ac{3(N-1)^2}{(N-2)(N-3)}. ] The above evaluates to 0 for a Gaussian distribution.

returns the minimum sample value

returns the maximum sample value

Expectation value of a function $ f $ on a given range $ mathcal{R} $, i.e., [ mathrm{E}left[f ;|; mathcal{R}right] = ac{sum_{x_i in mathcal{R}} f(x_i) w_i}{ sum_{x_i in mathcal{R}} w_i}. ] The 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.

$ y $-th percentile, defined as the value $ bar{x} $ such that [ y = ac{sum_{x_i < bar{x}} w_i}{ sum_i w_i} ]

Precondition:

$ y $-th top percentile, defined as the value $ bar{x} $ such that [ y = ac{sum_{x_i > bar{x}} w_i}{ sum_i w_i} ]

Precondition:

adds a datum to the set, possibly with a weight

Precondition:

weights must be positive or null