ROBUST 2(k) FACTORIAL DESIGNS: NON-NORMAL SYMMETRIC DISTRIBUTIONS

Abstract

Senoglu (2005, 2007a) considered the estimation and hypothesis testing procedures for the 2(k) factorial designs when the error distributions are skew. They obtained the modified maximum likelihood (MML) estimators of the model parameters and proposed new test statistics based on them. They show that their solutions are more efficient and robust than the classical solutions based on the least squares (LS) estimators via Monte Carlo simulation. In this study, we extend their studies to non-normal symmetric error distributions and develop new test statistics for testing the main effects and the interactions. Moreover, a real life example is given to illustrate the theoretical results.