/usr/include/ql/math/statistics/incrementalstatistics.hpp is in libquantlib0-dev 1.1-2build1.
This file is owned by root:root, with mode 0o644.
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/*
Copyright (C) 2003 Ferdinando Ametrano
Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
/*! \file incrementalstatistics.hpp
\brief statistics tool based on incremental accumulation
*/
#ifndef quantlib_incremental_statistics_hpp
#define quantlib_incremental_statistics_hpp
#include <ql/utilities/null.hpp>
#include <ql/errors.hpp>
namespace QuantLib {
//! Statistics tool based on incremental accumulation
/*! It can accumulate a set of data and return statistics (e.g: mean,
variance, skewness, kurtosis, error estimation, etc.)
\warning high moments are numerically unstable for high
average/standardDeviation ratios.
*/
class IncrementalStatistics {
public:
typedef Real value_type;
IncrementalStatistics();
//! \name Inspectors
//@{
//! number of samples collected
Size samples() const;
//! sum of data weights
Real weightSum() const;
/*! returns the mean, defined as
\f[ \langle x \rangle = \frac{\sum w_i x_i}{\sum w_i}. \f]
*/
Real mean() const;
/*! returns the variance, defined as
\f[ \frac{N}{N-1} \left\langle \left(
x-\langle x \rangle \right)^2 \right\rangle. \f]
*/
Real variance() const;
/*! returns the standard deviation \f$ \sigma \f$, defined as the
square root of the variance.
*/
Real standardDeviation() const;
/*! returns the error estimate \f$ \epsilon \f$, defined as the
square root of the ratio of the variance to the number of
samples.
*/
Real errorEstimate() const;
/*! returns the skewness, defined as
\f[ \frac{N^2}{(N-1)(N-2)} \frac{\left\langle \left(
x-\langle x \rangle \right)^3 \right\rangle}{\sigma^3}. \f]
The above evaluates to 0 for a Gaussian distribution.
*/
Real skewness() const;
/*! returns the excess kurtosis, defined as
\f[ \frac{N^2(N+1)}{(N-1)(N-2)(N-3)}
\frac{\left\langle \left(x-\langle x \rangle \right)^4
\right\rangle}{\sigma^4} - \frac{3(N-1)^2}{(N-2)(N-3)}. \f]
The above evaluates to 0 for a Gaussian distribution.
*/
Real kurtosis() const;
/*! returns the minimum sample value */
Real min() const;
/*! returns the maximum sample value */
Real max() const;
/*! returns the downside variance, defined as
\f[ \frac{N}{N-1} \times \frac{ \sum_{i=1}^{N}
\theta \times x_i^{2}}{ \sum_{i=1}^{N} w_i} \f],
where \f$ \theta \f$ = 0 if x > 0 and
\f$ \theta \f$ =1 if x <0
*/
Real downsideVariance() const;
/*! returns the downside deviation, defined as the
square root of the downside variance.
*/
Real downsideDeviation() const;
//@}
//! \name Modifiers
//@{
//! adds a datum to the set, possibly with a weight
/*! \pre weight must be positive or null */
void add(Real value, Real weight = 1.0);
//! adds a sequence of data to the set, with default weight
template <class DataIterator>
void addSequence(DataIterator begin, DataIterator end) {
for (;begin!=end;++begin)
add(*begin);
}
//! adds a sequence of data to the set, each with its weight
/*! \pre weights must be positive or null */
template <class DataIterator, class WeightIterator>
void addSequence(DataIterator begin, DataIterator end,
WeightIterator wbegin) {
for (;begin!=end;++begin,++wbegin)
add(*begin, *wbegin);
}
//! resets the data to a null set
void reset();
//@}
protected:
Size sampleNumber_, downsideSampleNumber_;
Real sampleWeight_, downsideSampleWeight_;
Real sum_, quadraticSum_, downsideQuadraticSum_,
cubicSum_, fourthPowerSum_;
Real min_, max_;
};
}
#endif
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