Profile Construction and Outlier Detection for Mixed-Type Data

What is it about?

In this work, we are interested in the construction of robust profiles for mixed-type data using a proper MDS configuration. To this end, we propose to compare different MDS configurations (coming from different metrics) through a combination of sensitivity and robust analysis. In particular, as an alternative to classical Gower’s metric, we propose a robust joint metric combining different distance matrices, avoiding redundant information, via related metric scaling. The search for robustness and iden-tification of outliers is done through a distance-based procedure related to geometric variability notions.

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Why is it important?

The proposed techniques are applied to a real data set provided by the largest humanitarian organi-zation involved in social programs in Spain. We find, in a robust way, the most relevant factors defining the profiles of people that were under risk of being socially excluded in the beginning of the 2008 economic crisis.