In the case of multicollinearity and outliers in regression analysis, the researchers are encouraged to deal with two problems simultaneously. Biased methods based on robust estimators are useful for estimating the regression coefficients for such cases. In this study we examine some robust biased estimators on the datasets with outliers in x direction and outliers in both x and y direction from literature by means of the R package ltsbase. Instead of a complete data analysis, robust biased estimators are evaluated using capabilities and features of this package.Key words: Biased Estimator, Least Trimmed Squares, Robust Estimation. ResumenEn el caso de multicolinealidad y outliers en análisis de regresión, los investigadores se enfrentan a tener que tratar dos problemas de manera simultánea. Métodos sesgados basados en estimadores robustos son útiles para estimar los coeficientes de regresión en estos casos. En este estudio se examinan algunos estimadores sesgados robustos en conjuntos de datos con outliers en x y outliers tanto en x como en y por medio del paquete ltsbase de R. En lugar de un análisis de datos completos, los estimadores sesgados robustos son evaluados usando las capacidades y características de este paquete.Palabras clave: estimadores sesgados, mínimos cuadrados recortados, robusta estimación.a Professor.

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There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.