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### IMSL_KOLMOGOROV2

IMSL_KOLMOGOROV2

The IMSL_KOLMOGOROV2 function performs a Kolmogorov-Smirnov two- sample test.

This routine requires an IDL Advanced Math and Stats license. For more information, contact your sales or technical support representative.

The IMSL_KOLMOGOROV2 function computes Kolmogorov-Smirnov two-sample test statistics for testing that two continuous cumulative distribution functions (CDF's) are identical based upon two random samples. One- or two-sided alternatives are allowed. If n_observations_x = N_ELEMENTS(x) and n_observations_y = N_ELEMENTS(y), then the exact p-values are computed for the two-sided test when n_observations_x * n_observations_y is less than 104.

Let Fn(x) denote the empirical CDF in the X sample, let Gm(y) denote the empirical CDF in the Y sample, where n = n_observations_x- NMISSINGX and m = n_observations_y NMISSINGY, and let the corresponding population distribution functions be denoted by F(x) and G(y), respectively. Then, the hypotheses tested by IMSL_KOLMOGOROV2 are as follows:

• H0 : F (x) = G (x)     H1 :F (x) ≠ G (x)
• H0 : F (x) ≥ G (x)     H1 : F (x) < G (x)
• H0 : F (x) ≤ G (x)     H1 : F (x) > G (x)

The test statistics are given as follows:

Asymptotically, the distribution of the statistic

(returned in Result (0)) converges to a distribution given by Smirnov (1939).

Exact probabilities for the two-sided test are computed when m * n is less than or equal to 104, according to an algorithm given by Kim and Jennrich (1973;). When m * n is greater than 104, the very good approximations given by Kim and Jennrich are used to obtain the two-sided p-values. The one-sided probability is taken as one half the two-sided probability. This is a very good approximation when the p-value is small (say, less than 0.10) and not very good for large p-values.

## Example

The following example illustrates the IMSL_KOLMOGOROV2 routine with two randomly generated samples from a uniform(0,1) distribution. Since the two theoretical distributions are identical, we would not expect to reject the null hypothesis.

IMSL_RANDOMOPT, set	=	123457
x	=	IMSL_RANDOM(100, /Uniform)
y	=	IMSL_RANDOM(60, /Uniform)
stats	=	IMSL_KOLMOGOROV2(x, y, DIFFERENCES = d, \$
NMISSINGX = nmx, NMISSINGY = nmy)
PRINT, 'D	=', d(0)
PRINT, 'D+ =', d(1) PRINT, 'D- =', d(2)
PRINT, 'Z	=', stats(0)
PRINT, 'Prob greater D one sided =', stats(1)
PRINT, 'Prob greater D two sided =', stats(2)
PRINT, 'Missing X =', nmx
PRINT, 'Missing Y =', nmy

D	=    0.180000
D+	=   0.180000
D-	=   0.0100001
Z	=    1.10227
Prob greater D one sided =    0.0720105
Prob greater D two sided =    0.144021
Missing X =    0
Missing Y =    0

## Syntax

Result = KOLMORGOROV2(X, Y [, DIFFERENCES=variable] [, /DOUBLE] [, NMISSINGX=variable] [, NMISSINGY=variable])

## Return Value

One-dimensional array of length 3 containing Z, p1, and p2.

## Arguments

### X

One-dimensional array containing the observations from sample one.

### Y

One-dimensional array containing the observations from sample two.

## Keywords

### DIFFERENCES (optional)

Named variable into which a one-dimensional array containing Dn, Dn, Dn is stored.

### DOUBLE (optional)

If present and nonzero, then double precision is used.

### NMISSINGX (optional)

Named variable into which the number of missing values in the x sample is stored.

### NMISSINGY (optional)

Named variable into which the number of missing values in the y sample is stored.

## Version History

 6.4 Introduced

## See Also

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