Abstract
Outlier detection can serve as an extremely important tool for researchers from a wide range of fields. From the sectors of banking and marketing to the social sciences and healthcare sectors, outlier detection techniques are very useful for identifying subjects that exhibit different and sometimes peculiar behaviours. When the data set available to the researcher consists of both discrete and continuous variables, outlier detection presents unprecedented challenges. In this talk we discuss a novel approach to outlier identification in settings of mixed-type data. The talk consists of two main parts, the first being the problem of flagging outlying observations in the continuous and the discrete spaces independently and the second being the task of detecting anomalies in the mixed-attribute domain. We achieve promising results in a series of simulations on artificially generated data sets and highlight potential challenges that may arise.
Collegamento Microsoft Teams
Organizzazione
Christian Hennig