Kolmogorov-Smirnoff test implementation

[Dupuis]

Hello,

while trying to understand the logic behind KS test, I noticed Octave
implementation is 
  ks   = sqrt (n) * max (max ([abs(z - (0:(n-1))/n); abs(z - (1:n)/n)]));
    pval = 1 - kolmogorov_smirnov_cdf (ks);
where z is the value of the cumulative distribution of points of the sorted
input.

I looked a bit in google, and found references at NIST
(http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm) and in a
1971 paper (On Kolmogorov-Smirnov Type One-Sample Statistics,
Urs R. Maag and Ghislaine Dicaire, Biometrika, Vol. 58, No. 3 (Dec., 1971),
pp. 653-656) : both use the definition
ks = sqrt(n)*max(max([z - (0:(n-1))/n; (1:n)/n - z]))

The difference in Octave case is that it takes the maximum of the absolute
values of each vector of differences,  which to me seems stronger than the
NIST implementation. I tried on a randomly generated vector and both
definitions gave the same result. 

Could someone please confirm that the Octave implementation does not
overestimate the discrepancy between theoretical and empirical cdf ?

Regards

Pascal
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