Data depth for mixed-type data through MDS. An application to biological age imputation
Year: 2024
Type: article
Abstract: For a mixed-type dataset, we propose a new procedure to assess the quality of an observation as a central tendency. Next, we apply this technique to valuate the functional condition of a human organism in terms of its biological age, which is based on biomarkers, medical conditions, life habits, and sociodemographic variables. These records are of mixed type since they are made up by numerical and categorical variables. In order to evaluate the centrality of an observation in a mixed-type dataset, we obtain a Multidimensional Scaling representation and use some classical notion of multivariate data depth in an appropriate space. The biological age of an individual is finally assessed in terms of the age that would make it as deep as possible with respect to a sample of individuals of a similar age subject to it retaining all other features unchanged. • Biological age assessment based on data depth. • Depth notions for mixed-type data. • Brief discussion on the factors of the ageing process.
Source: Socio-Economic Planning Sciences
Authors: Ignacio Cascos, Aurea Grané, Jingye Qian
Institution: Universidad Carlos III de Madrid
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,020
OpenAlex ID: W4405738617