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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.