Abstract
This article focuses on the idea of recognizing distributions rather than performing classic goodness-of-fit tests (GoFTs). In order to recognize distributions, the method of potential functions (MoPF) is used, focusing the reader’s attention on recognizing the normal distribution. The prevailing part of the article concentrates on the implementation of a classifier of distributions that involves MoPF. Recognizing distributions is supported by numerous examples of simulation and real data examples. GoFTs are conservative. When the test statistics exceeds relevant critical value, there are reasons to reject What next? The answer is: Recognizing distributions by means of the MoPF.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
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.
