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# PetscProbComputeKSStatisticWeighted

Compute the Kolmogorov-Smirnov statistic for the weighted empirical distribution for an input vector, compared to an analytic CDF.

## Synopsis

    #include "petscdt.h"
    PetscErrorCode PetscProbComputeKSStatisticWeighted(Vec v, Vec w, PetscProbFn *cdf, PetscReal *alpha)

Collective

## Input Parameters

- v - The data vector, blocksize is the sample dimension

- w - The vector of weights for each sample, instead of the default 1/n

- cdf - The analytic CDF

## Output Parameter

- alpha - The KS statistic

## Notes

The Kolmogorov-Smirnov statistic for a given cumulative distribution function \(F(x)\) is

\[ D_n = \sup_x \left| F_n(x) - F(x) \right| \]

where \(\sup_x\) is the supremum of the set of distances, and the empirical distribution function \(F_n(x)\) is discrete, and given by

\[ F_n = # of samples <= x / n \]

The empirical distribution function \(F_n(x)\) is discrete, and thus had a ``stairstep’’ cumulative distribution, making \(n\) the number of stairs. Intuitively, the statistic takes the largest absolute difference between the two distribution functions across all \(x\) values.

The goodness-of-fit test, or Kolmogorov-Smirnov test, is constructed using the Kolmogorov distribution. It rejects the null hypothesis at level \(\alpha\) if

\[ \sqrt{n} D_{n} > K_{\alpha}, \]

where \(K_\alpha\) is found from

\[ \operatorname{Pr}(K \leq K_{\alpha}) = 1 - \alpha. \]

This means that getting a small alpha says that we have high confidence that the data did not come from the input distribution, so we say that it rejects the null hypothesis.

## See Also

`PetscProbComputeKSStatistic()`, `PetscProbComputeKSStatisticMagnitude()`, `PetscProbFn`

## Level

advanced

## Location

src/dm/dt/interface/dtprob.c

Index of all DT routines
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Index of all manual pages