Multiple members of our research unit attended the Austrian and Slovenian Statistical Days 2022, öffnet eine externe URL in einem neuen Fenster in Graz and we proudly announce that Sanja Priselac received the award from the Austrian Statistical Society in the section Applied Statistics, öffnet eine externe URL in einem neuen Fenster for her excellent master's thesis, which is summarized below. Congratulations!

Outlier-robust Logistic Regression for Imbalanced Data

The master's thesis proposes a robust logistic regression for data sets with an imbalanced distribution of the output variable based on the Bianco-Yohai estimator. The imbalance learning problem is addressed by including the cost-sensitive features in the objective function for parameter estimation. Moreover, the thesis also introduces an additional method for detecting leverage points required for the weighted version of the estimator, which significantly extends the data domain in which the Bianco-Yohai estimator is applicable.

The obtained cost-sensitive forms of the Bianco-Yohai estimator, in the weighted and original versions, are compared with the corresponding non-robust and non-cost-sensitive forms. The results of the simulation experiments and the use case with the imbalanced data set employed for credit scoring show that the cost-sensitive form of the Bianco-Yohai estimator, in both its original and weighted versions, provides a statistically reliable classifier for modeling imbalanced data containing outliers.