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Smart Visualization of Mixed Data

Author

Listed:
  • Aurea Grané

    (Department of Statistics, University Carlos III of Madrid, 28903 Getafe, Spain)

  • Giancarlo Manzi

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy)

  • Silvia Salini

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy)

Abstract

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020–2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided.

Suggested Citation

  • Aurea Grané & Giancarlo Manzi & Silvia Salini, 2021. "Smart Visualization of Mixed Data," Stats, MDPI, vol. 4(2), pages 1-14, June.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:2:p:29-485:d:567027
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    References listed on IDEAS

    as
    1. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    2. Anthony Atkinson & Marco Riani, 2004. "The forward search and data visualisation," Computational Statistics, Springer, vol. 19(1), pages 29-54, February.
    3. Aurea Grané & Alpha A. Sow-Barry, 2021. "Visualizing Profiles of Large Datasets of Weighted and Mixed Data," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
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    Cited by:

    1. Fabian Lindner & Gerald Reiner & Sophia Keil, 2025. "A behavioral perspective on visualization in manufacturing and operations management: a review, framework, and research agenda," Operations Management Research, Springer, vol. 18(1), pages 317-352, March.
    2. 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).
    3. Boj, Eva & Grané, Aurea, 2024. "The robustification of distance-based linear models: Some proposals," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    4. Reiko Aoki & Juan P. M. Bustamante & Gilberto A. Paula, 2022. "Local influence diagnostics with forward search in regression analysis," Statistical Papers, Springer, vol. 63(5), pages 1477-1497, October.

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