Abstract
We present and implement an algorithm for computing the parameter estimates in a univariate probability model for a continuous random variable that minimizes the Kolmogorov–Smirnov test statistic. The algorithm uses an evolutionary optimization technique to solve for the estimates. Several simulation experiments demonstrate the effectiveness of this approach.
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