In the context of polytomous regression, as with any generalized linear model, robustness issues are well documented. Existing robust estimators designed to protect against misclassification, but do not outlying covariates. It is shown that this can have a much bigger impact on estimation and testing than misclassification alone. To address problem, two new introduced: model-type estimator an o...

An introduction to robustness in statistics, with emphasis on its relevance to regression analysis. The weaknesses of the least squares estimator are highlighted, and the idea of error in data re ned. Properties such as breakdown, e ciency and equivariance are discussed and, through consideration of M, S and MM-estimators in relation to these properties, the progressive nature of robust estimat...

1. Introduction One of the most important statistical tools is a linear regression analysis for many fields. Nearly all regression analysis relies on the method of least squares for estimation of the parameters in the model. A problem that we often encountered in the application of regression is the presence of an outlier or outliers in the data. Outliers can be generated by from a simple opera...

Abstract Zaman and Bulut (2018a) developed a class of estimators for population mean utilising LMS robust regression supplementary attributes. In this paper, family is proposed, based on the adaptation presented by (2019), followed introduction new regression-type tools (LAD, H-M, LMS, H-MM, Hampel-M, Tukey-M, LTS) The square error expressions adapted proposed families are determined through ge...