Recognizing distributions rather than goodness-of-fit testing. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Sulewski, Piotr
• Is Part Of: Communications in statistics. Volume 51:Issue 11(2022)
• Page Start: 6701
• Page End: 6714
• Publication Date: 2022-11-02
• Subjects: Goodness-of-fit test -- k-nearest neighbors rule -- Monte Carlo method -- Recognizing distribution -- Skewness and excess kurtosis; 62G10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2020.1812647
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: This article puts forward an idea of recognizing distributions rather than carrying-out classic goodness-of-fit tests (GoFTs). For the purpose of recognizing the k-nearest neighbors (kNN) rule is applied. We focus the reader's attention on recognizing the normal distribution. The main part of the article is devoted to the computer implementation of a classifier of distributions that involves kNN rule. GoFTs are conservative. Recognizing distributions is exemplified by simulation and real data examples. When the test statistics exceeds relevant critical value then the verdict sounds: there are reasons to reject H 0 . And what next? Recognizing distributions is the answer to this question.