yat  0.12.3pre
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theplu::yat::statistics::KolmogorovSmirnovOneSample Class Reference

Kolmogorov Smirnov Test for one class. More...

#include <yat/statistics/KolmogorovSmirnovOneSample.h>

Public Member Functions

 KolmogorovSmirnovOneSample (void)
 Constructor.
 
void add (double value, double weight=1.0)
 add a value More...
 
double p_value (void) const
 Large-Sample Approximation. More...
 
void remove (double value, double weight=1.0)
 Remove a data point. More...
 
void reset (void)
 resets everything to zero
 
double score (void) const
 Kolmogorov Smirnov statistic. More...
 
double signed_score (void) const
 

Friends

std::ostream & operator<< (std::ostream &, const KolmogorovSmirnovOneSample &)
 

Detailed Description

Kolmogorov Smirnov Test for one class.

Class can be used to test if data follows standard uniform distribution. Other distributions are not supported directly, but it is possible to check against other distribution by tranforming the data. If, for example, X is standard exponentially distributed log(X) is standard uniform. Therefore, rather than testing if X is exponential it is equivalent to test if log(X) is standard uniform.

Since
New in yat 0.11

Member Function Documentation

void theplu::yat::statistics::KolmogorovSmirnovOneSample::add ( double  value,
double  weight = 1.0 
)

add a value

value should be a value in range [0, 1].

double theplu::yat::statistics::KolmogorovSmirnovOneSample::p_value ( void  ) const

Large-Sample Approximation.

This analytical approximation of p-value can be used when all weight equal unity and sample size n is large. The p-value is calcuated as $ P = \displaystyle - 2 \sum_{k=1}^{\infty} (-1)^ke^{-2k^2s^2n}$, where s is the score and n is the sum of weights.

void theplu::yat::statistics::KolmogorovSmirnovOneSample::remove ( double  value,
double  weight = 1.0 
)

Remove a data point.

Exceptions
utility::runtime_errorif no data point exist with value and weight.
double theplu::yat::statistics::KolmogorovSmirnovOneSample::score ( void  ) const

Kolmogorov Smirnov statistic.

$ sup_x | F(x) - x | $ where is the empirical cumulative distribution $ F(x) = \frac{\sum_{i:x_i\le x}w_i}{ \sum w_i} $

double theplu::yat::statistics::KolmogorovSmirnovOneSample::signed_score ( void  ) const

Same as score() but keeping the sign, in other words, abs(signed_score())==score()

A positive score implies that values on average are smaller 0.5.


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

Generated on Mon Jun 1 2015 12:29:53 for yat by  doxygen 1.8.5