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Career-path analysis using drifting Markov models (DMM) and self-organizing maps

Author

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  • Sébastien Massoni

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Madalina Olteanu

    (SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Patrick Rousset

    (CEREQ - Centre d'études et de recherches sur les qualifications - ministère de l'Emploi, cohésion sociale et logement - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche)

Abstract

Analyzing school-to-work transitions is an important challenge for the specialists of the labor-market. The aim of this paper is to study the insertion of graduates and to identify the main career-paths typologies. We introduce a new methodology for clustering career-paths by combining statistical estimation of non-homogeneous Markov chains with self-organizing maps. The proposed methodology is tested on real-life data issued from the survey ''Generation 98'' elaborated by CEREQ, France (http://www.cereq.fr/)

Suggested Citation

  • Sébastien Massoni & Madalina Olteanu & Patrick Rousset, 2010. "Career-path analysis using drifting Markov models (DMM) and self-organizing maps," Post-Print hal-00443530, HAL.
    • Handle: RePEc:hal:journl:hal-00443530
    Note: View the original document on HAL open archive server: https://hal.science/hal-00443530
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    References listed on IDEAS

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    1. Vergne Nicolas, 2008. "Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-45, February.
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