Handling Outlier in Two-Ways Table by Robust Alternating Regression of FANOVA Models: Towards Robust AMMI Models

Abstract

AMMI (Additive Main Effect Multiplicative Interaction) model for interactions in two-way table provide the major mean for studying stability and adaptability through genotype × environment interaction (GEI), which modeled by full interaction model. Eligibility of AMMI model depends on that assumption of normally independentdistributederrorwithaconstantvariance. Nowadays,AMMImodelshavebeendevelopedforany conditionofMETdatawhich violencethenormality,homogeneityassumpion. Wecanmentioninthisclassof modelling as M-AMMI for mixed AMMI models, G-AMMI for generalized AMMI models. The G-AMMI was handling non-normality i.e categorical response variables using an algorithm of alternating regression. While in handling the non-homogeneity in mix-models sense, one may use a model called factor analytic multiplicative. The development of AMMI models is also to handle any outlier that might be found coincides withnon-homogeneityconditionofthedata. Inthispaper,wewillpresentofhandlingoutlierinmultplicative model by robust approach of alternating regression algorithm.

Keywords

• ammi
• g-ammi
• m-ammi
• factor analytic
• multiplicative models
• alternating regression
• robust approach