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NAG Library Function Document

nag_prob_2_sample_ks (g01ezc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

nag_prob_2_sample_ks (g01ezc) returns the probability associated with the upper tail of the Kolmogorov–Smirnov two sample distribution.

2

Specification

#include <nag.h>

#include <nagg01.h>

double

nag_prob_2_sample_ks (Integer n1, Integer n2, double d, NagError *fail)

3

Description

Let

F_{n_{1}} (x)

and

G_{n_{2}} (x)

denote the empirical cumulative distribution functions for the two samples, where

n_{1}

and

n_{2}

are the sizes of the first and second samples respectively.

The function nag_prob_2_sample_ks (g01ezc) computes the upper tail probability for the Kolmogorov–Smirnov two sample two-sided test statistic

D_{n_{1}, n_{2}}

, where

D_{n_{1}, n_{2}} = \sup_{x} |F_{n_{1}} (x) - G_{n_{2}} (x)| .

The probability is computed exactly if

n_{1}, n_{2} \leq 10000

and

\max (n_{1}, n_{2}) \leq 2500

using a method given by Kim and Jenrich (1973). For the case where

\min (n_{1}, n_{2}) \leq 10 %

of the

\max (n_{1}, n_{2})

and

\min (n_{1}, n_{2}) \leq 80

the Smirnov approximation is used. For all other cases the Kolmogorov approximation is used. These two approximations are discussed in Kim and Jenrich (1973).

4

References

Conover W J (1980) Practical Nonparametric Statistics Wiley

Feller W (1948) On the Kolmogorov–Smirnov limit theorems for empirical distributions Ann. Math. Statist. 19 179–181

Kendall M G and Stuart A (1973) The Advanced Theory of Statistics (Volume 2) (3rd Edition) Griffin

Kim P J and Jenrich R I (1973) Tables of exact sampling distribution of the two sample Kolmogorov–Smirnov criterion

D_{m n} (m < n)

Selected Tables in Mathematical Statistics 1 80–129 American Mathematical Society

Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill

Smirnov N (1948) Table for estimating the goodness of fit of empirical distributions Ann. Math. Statist. 19 279–281

5

Arguments

1: $n1$ – IntegerInput: On entry: the number of observations in the first sample, $n_{1}$ .

Constraint: $n1 \geq 1$ .
2: $n2$ – IntegerInput: On entry: the number of observations in the second sample, $n_{2}$ .

Constraint: $n2 \geq 1$ .
3: $d$ – doubleInput: On entry: the test statistic $D_{n_{1}, n_{2}}$ , for the two sample Kolmogorov–Smirnov goodness-of-fit test, that is the maximum difference between the empirical cumulative distribution functions (CDFs) of the two samples.

Constraint: $0.0 \leq d \leq 1.0$ .
4: $fail$ – NagError *Input/Output: The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_CONVERGENCE: The Smirnov approximation used for large samples did not converge in $200$ iterations. The probability is set to $1.0$ .
NE_INT: On entry, $n1 = 〈value〉$ and $n2 = 〈value〉$ .
Constraint: $n1 \geq 1$ and $n2 \geq 1$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_REAL: On entry, $d < 0.0$ or $d > 1.0$ : $d = 〈value〉$ .

7

Accuracy

The large sample distributions used as approximations to the exact distribution should have a relative error of less than 5% for most cases.

8

Parallelism and Performance

nag_prob_2_sample_ks (g01ezc) is not threaded in any implementation.

9

Further Comments

The upper tail probability for the one-sided statistics,

D_{n_{1}, n_{2}}^{+}

D_{n_{1}, n_{2}}^{-}

, can be approximated by halving the two-sided upper tail probability returned by nag_prob_2_sample_ks (g01ezc), that is

p / 2

. This approximation to the upper tail probability for either

D_{n_{1}, n_{2}}^{+}

D_{n_{1}, n_{2}}^{-}

is good for small probabilities, (e.g.,

p \leq 0.10

) but becomes poor for larger probabilities.

The time taken by the function increases with

n_{1}

and

n_{2}

, until

n_{1} n_{2} > 10000

\max (n_{1}, n_{2}) \geq 2500

. At this point one of the approximations is used and the time decreases significantly. The time then increases again modestly with

n_{1}

and

n_{2}

10

Example

The following example reads in

10

different sample sizes and values for the test statistic

D_{n_{1}, n_{2}}

. The upper tail probability is computed and printed for each case.

NAG Library Function Document

nag_prob_2_sample_ks (g01ezc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results