Comparison of Type I Error of some Non-Parametric Tests on Multiple Regression Models Coefficients

Abstract

Various non-parametric methods have been used to perform hypothesis test on multiple regression coefficients. In this article, at first the most important methods which has been introduced from other statisticians as proper methods, such as Kennedy, Freedman and Lane, and modified Kennedy, are explained and then, Freedman and Lane (Huh-John) method will be modified in the form of Kennedy method; finally, all aforementioned methods will be compared as simulating. At last, we look for a method that done best. So, Huh-John (2001) modify the method of Kennedy which was proposed in 1995 and showed by simulation that is called modified Huh-John method; and it has less type I error. On the other hand, Anderson as simulation (1991) and Schadrekh as theory (2011) had shown that Freedman& Lane method has lower type I error in comparison with Kennedy method. We did some modification on Freedman and Lane method that Huh-John had done on Kennedy method and compared this modified method with Freedman and Lane and Huh-John method. We conclude that Freedman and Lane modified method often has lower type I error estimation and higher power than Freedman& Lane and Huh-John method.