npstat::BandwidthCVPseudoLogliND< Num, Array > Class Template Reference

#include <BandwidthCVPseudoLogliND.hh>

Inheritance diagram for npstat::BandwidthCVPseudoLogliND< Num, Array >:

Public Member Functions
	BandwidthCVPseudoLogliND (double regularizationPower=0.5)
unsigned long	getNonZeroCount () const
unsigned long	getRenormCount () const
virtual double	operator() (const HistoND< Num > &histo, const Array &densityEstimate, const AbsPolyFilterND &filterUsed) const
virtual double	operator() (const HistoND< Num > &histo, double effectiveSampleSize, const Array &densityEstimate, const AbsPolyFilterND &filterUsed) const

Additional Inherited Members
Public Types inherited from npstat::AbsBandwidthCVND< Num, Array >
typedef Num	bin_type
typedef Array	density_type

Detailed Description

template<typename Num, class Array>
class npstat::BandwidthCVPseudoLogliND< Num, Array >

Class for calculating KDE or LOrPE cross-validation pseudo log likelihood, for multivariate density estimates

Constructor & Destructor Documentation

BandwidthCVPseudoLogliND()

template<typename Num , class Array >

npstat::BandwidthCVPseudoLogliND< Num, Array >::BandwidthCVPseudoLogliND ( double  regularizationPower = 0.5 )

inlineexplicit

Parameter "regularizationPower" is used to limit the contributions into the overall pseudo log likelihood from points for which the "leaving one out" density is very low. For those points, instead of "leaving one out" density, we will use the density generated by that point itself divided by pow(N, regularizationPower). This method limits the effect of tails on bandwidth determination.

Member Function Documentation

operator()() [1/2]

template<typename Num , class Array >

virtual double npstat::BandwidthCVPseudoLogliND< Num, Array >::operator() ( const HistoND< Num > &  histo, const Array &  densityEstimate, const AbsPolyFilterND &  filterUsed  ) const

virtual

It should be assumed that the "nFillsInRange" method of the argument histogram returns the actual number of fills.

"densityEstimate" is allowed to be an estimate without truncation (even if it includes negative values).

Implements npstat::AbsBandwidthCVND< Num, Array >.

operator()() [2/2]

template<typename Num , class Array >

virtual double npstat::BandwidthCVPseudoLogliND< Num, Array >::operator() ( const HistoND< Num > &  histo, double  effectiveSampleSize, const Array &  densityEstimate, const AbsPolyFilterND &  filterUsed  ) const

virtual

Cross-validation for samples of multivariate weighted points

Implements npstat::AbsBandwidthCVND< Num, Array >.

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The documentation for this class was generated from the following file:

• stat/BandwidthCVPseudoLogliND.hh