Numerically stabilised general linear least squares. More...

#include <qle/math/stabilisedglls.hpp>

Public Types
enum	Method { None , MaxAbs , MeanStdDev }

Public Member Functions
template<class xContainer , class yContainer , class vContainer >
	StabilisedGLLS (const xContainer &x, const yContainer &y, const vContainer &v, const Method method=MeanStdDev)

const Array &	transformedCoefficients () const

const Array &	transformedResiduals () const

const Array &	transformedStandardErrors () const

const Array &	transformedError () const

const Array &	xMultiplier () const
	Transformation parameters (u => (u + shift) * multiplier for u = x, y)

const Array &	xShift () const

const Real	yMultiplier () const

const Real	yShift () const

Size	size () const

Size	dim () const

template<class xType , class vContainer >
Real	eval (xType x, vContainer &v, typename boost::enable_if< typename boost::is_arithmetic< xType >::type >::type *=0)
	evaluate regression function in terms of original x, y

template<class xType , class vContainer >
Real	eval (xType x, vContainer &v, typename boost::disable_if< typename boost::is_arithmetic< xType >::type >::type *=0)
	evaluate regression function in terms of original x, y

Protected Member Functions
template<class xContainer , class yContainer , class vContainer >
void	calculate (xContainer x, yContainer y, vContainer v, typename boost::enable_if< typename boost::is_arithmetic< typename xContainer::value_type >::type >::type *=0)

template<class xContainer , class yContainer , class vContainer >
void	calculate (xContainer x, yContainer y, vContainer v, typename boost::disable_if< typename boost::is_arithmetic< typename xContainer::value_type >::type >::type *=0)

Protected Attributes
Array	a_

Array	err_

Array	residuals_

Array	standardErrors_

Array	xMultiplier_

Array	xShift_

Real	yMultiplier_

Real	yShift_

Method	method_

boost::shared_ptr< GeneralLinearLeastSquares >	glls_

Detailed Description

Numerically stabilised general linear least squares.

The input data is linearly transformed before performing the linear least squares fit. The linear least squares fit on the transformed data is done using the GeneralLinearLeastSquares class.

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description