Biological Age Imputation by Data Depth: A Proposal and Some Preliminary Results

  1. Cabras, Stefano 1
  2. Cascos, Ignacio 1
  3. D’Auria, Bernardo 2
  4. Durbán, María 1
  5. Guerrero, Vanesa 1
  6. Ochoa, Maicol 1
  1. 1 Universidad Carlos III de Madrid
    info

    Universidad Carlos III de Madrid

    Madrid, España

    ROR https://ror.org/03ths8210

  2. 2 Department of Economics, Universit`a degli Studi “G. d’Annunzio”,Chieti-Pescara, Italy
Libro:
Building Bridges between Soft and Statistical Methodologies for Data Science

Editorial: Springer

ISSN: 2194-5357,  2194-5365

ISBN: 9783031155086,  9783031155093

Año de publicación: 2022

Páginas: 57-64

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-15509-3_8 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

The biological age is an indicator of the functional conditionof an individual’s body. Unlike the chronological age, which just measures the time from birth, the biological age of a human is also affectedby its medical condition, life habits, some sociodemographic variables, aswell as biomarkers. Taking advantage of the statistical concept of depth,which serves as a measurement of the degree of centrality of a multivariate observation with respect to a dataset, we assess the biological ageof an individual as the chronological age that would make her selectedrecords as deep as possible when compared with those of other individuals with a chronological age similar to hers. Some direct conclusions ofthis imputation technique are presented.

Referencias bibliográficas

  • Cascos, I., Ochoa, M.: Expectile depth: theory and computation for bivariate datasets. J. Multivar. Anal. 184, 104757 (2021)
  • Hallin, M., Paindaveine, D., Šiman, M.: Multivariate quantiles and multiple-output regression: from $$L_1$$ optimization to halfspace depth. Ann. Stat. 38, 635–669 (2010)
  • Koshevoy, G., Mosler, K.: Zonoid trimming for multivariate distributions. Ann. Stat. 25, 1998–2017 (1997)
  • Mosler, K.: Depth statistics. In: Becker, C., Fried, R., Kuhnt, S. (eds.) Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather, pp. 17–34. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35494-6_2
  • Mozharovskyi, P., Josse, J., Husson, F.: Nonparametric imputation by data depth. J. Am. Stat. Assoc. 115, 241–253 (2020)
  • Rahman, S.A., Giacobbi, P., Pyles, L., Mullett, C., Doretto, G., Adjeroh, D.A.: Deep learning for biological age estimation. Brief. Bioinform. 22, 1767–1781 (2021)
  • Rodríguez-Pardo, J.M., López-Farré, A.: Longevity and aging in the third millennium: new perspectives. Fundación MAPFRE, Madrid (2017)
  • Tukey, J.W.: Mathematics and the picturing of data. In: Proceedings of the International Congress of Mathematicians, Vancouver, B.C., 1974, vol. 2, pp 523–531. Canadian Mathematical Congress, Montreal (1975)
  • United Nations General Assembly, Transforming our world: the 2030 Agenda for Sustainable Development, A/RES/70/1, 21 October 2015
  • United Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2019: Highlights, ST/ESA/SER.A/430 (2019)
  • United Nations, Department of Economic and Social Affairs and Institute for Training and Research. Stakeholder engagement & the 2030 Agenda. A practical guide (2020)
  • Voitenko, V.P., Tokar, A.V.: The assessment of biological age and sex differences of human aging. Exp. Aging Res. 9, 239–244 (1983)
  • World Health Organization: World report on ageing and health. WHO Library, Luxembourg (2015)
  • Zhang, W.-G.: Construction of an integral formula of biological age for a healthy Chinese population using principle component analysis. J. Nutrition Health Aging 18(2), 137–142 (2014). https://doi.org/10.1007/s12603-013-0345-8
  • Zhang, J.L., Chen, X.: Common methods of biological age estimation. Clin. Interv. Aging 12, 759–772 (2017)
  • Zuo, Y., Serfling, R.: General notions of statistical depth function. Ann. Stat. 28, 461–482 (2000)