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The normality assumption on data set is very restrictive approach formodelling. The generalized form of normal distribution, named as an exponentialpower (EP) distribution, and its scale mixture form have been consideredextensively to overcome the problem for modelling non-normal data set sincelast decades. However, examining the robustness properties of maximumlikelihood (ML) estimators of parameters in these distributions, such as the inuence function, gross-error sensitivity, breakdown point andinformation-standardized sensitivity, has not been considered together. Thewell-known asymptotic properties of ML estimators of location, scale and addedskewness parameters in EP and its scale mixture form distributions are studiedand also these ML estimators for location, scale and scale variant (skewness)parameters can be represented as an iterative reweighting algorithm to computethe estimates of these parameters simultaneously.

On the Robustness and Asymptotic Properties for Maximum Likelihood  Estimators of Parameters in Exponential Power and its Scale Mixture Form  Distributions