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Data depth for mixed-type data through MDS. An application to biological age imputation

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  • Cascos, Ignacio
  • Grané, Aurea
  • Qian, Jingye

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.

Suggested Citation

  • Cascos, Ignacio & Grané, Aurea & Qian, Jingye, 2025. "Data depth for mixed-type data through MDS. An application to biological age imputation," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:soceps:v:98:y:2025:i:c:s0038012124003409
    DOI: 10.1016/j.seps.2024.102140
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    References listed on IDEAS

    as
    1. López-Pintado, Sara & Romo, Juan, 2009. "On the Concept of Depth for Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 718-734.
    2. Nieto-Reyes, Alicia & Battey, Heather, 2021. "A topologically valid construction of depth for functional data," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    3. Alexander H. Foss & Marianthi Markatou & Bonnie Ray, 2019. "Distance Metrics and Clustering Methods for Mixed‐type Data," International Statistical Review, International Statistical Institute, vol. 87(1), pages 80-109, April.
    4. Boj, Eva & Grané, Aurea, 2024. "The robustification of distance-based linear models: Some proposals," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    5. Cascos, Ignacio & Ochoa, Maicol, 2021. "Expectile depth: Theory and computation for bivariate datasets," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    6. Grané, Aurea & Salini, Silvia & Verdolini, Elena, 2021. "Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    7. Aurea Grané & Giancarlo Manzi & Silvia Salini, 2021. "Smart Visualization of Mixed Data," Stats, MDPI, vol. 4(2), pages 1-14, June.
    8. Pavlo Mozharovskyi & Julie Josse & François Husson, 2020. "Nonparametric Imputation by Data Depth," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 241-253, January.
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