What is a K-S model?
K-S or Kolmogorov-Smirnov chart measures performance of classification models. More accurately, K-S is a measure of the degree of separation between the positive and negative distributions.
What does KS mean in statistics?
Kolmogorov–Smirnov test
In statistics, the Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two …
What is a Ks value?
The Kolmogorov–Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples.
How is the KS test used in statistics?
How the KS Test Works 1 Descriptive Statistics. Thus descriptive statistics were developed to reduce the list of all the data items to a few simpler numbers. 2 Cumulative Fraction Function 3 Empirical Distribution Function. 4 Percentile Plot 5 Estimated Distribution Function Ogive. 6 A Final Example.
Where can I find the KS test in SAS?
The KS test and its p-values for discrete null distributions and small sample sizes are also computed in as part of the dgof package of the R language. Major statistical packages among which SAS PROC NPAR1WAY, Stata ksmirnov implement the KS test under the assumption that
What is the maximum deviation of the KS test?
The KS-test uses the maximum vertical deviation between the two curves as the statistic D. In this case the maximum deviation occurs near x =1 and has D =.45. (The fraction of the treatment group that is less then one is 0.2 (4 out of the 20 values); the fraction of the control group that is less than one is 0.65 (13 out of the 20 values).
Can I use Kolmogorov-Smirnov test and estimation?
If you’re wedded to a Kolmogorov-Smirnov type of test, you can take the approach of Lilliefors’ test. That is, to use the KS statistic but have the distribution of the test statistic reflect the effect of parameters estimation – simulate the distribution of the test statistic under parameter estimation.