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A variable selection procedure for depth measures

Author

Listed:
  • Agustín Alvarez

    (Universidad Nacional de General Sarmiento)

  • Marcela Svarc

    (Universidad de San Andrés and CONICET)

Abstract

We herein introduce variable selection procedures based on depth similarity, aimed at identifying a small subset of variables that can better explain the depth assigned to each point in space. Our study is not intended to deal with the case of high-dimensional data. Identifying noisy and dependent variables helps us understand the underlying distribution of a given dataset. The asymptotic behaviour of the proposed methods and numerical aspects concerning the computational burden are studied. Furthermore, simulations and a real data example are analysed.

Suggested Citation

  • Agustín Alvarez & Marcela Svarc, 2021. "A variable selection procedure for depth measures," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 247-271, June.
    • Handle: RePEc:spr:alstar:v:105:y:2021:i:2:d:10.1007_s10182-021-00391-y
    DOI: 10.1007/s10182-021-00391-y
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    References listed on IDEAS

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    1. Germán Caruso & Walter Sosa-Escudero & Marcela Svarc, 2015. "Deprivation and the Dimensionality of Welfare: A Variable-Selection Cluster-Analysis Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 702-722, December.
    2. Leonardo Gasparini & Walter Sosa-Escudero & Mariana Marchionni & Sergio Olivieri, 2013. "Multidimensional poverty in Latin America and the Caribbean: new evidence from the Gallup World Poll," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(2), pages 195-214, June.
    3. Giovanni Ferro Luzzi & Yves Flückiger & Sylvain Weber, 2008. "A Cluster Analysis of Multidimensional Poverty in Switzerland," Palgrave Macmillan Books, in: Nanak Kakwani & Jacques Silber (ed.), Quantitative Approaches to Multidimensional Poverty Measurement, chapter 4, pages 63-79, Palgrave Macmillan.
    4. Yang, Lin, 2018. "Measuring well-being: a multidimensional index integrating subjective well-being and preferences," LSE Research Online Documents on Economics 87789, London School of Economics and Political Science, LSE Library.
    5. Daniel Fraiman & Nicolas Fraiman & Ricardo Fraiman, 2017. "Nonparametric statistics of dynamic networks with distinguishable nodes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 546-573, September.
    6. Nanak Kakwani & Jacques Silber (ed.), 2008. "Quantitative Approaches to Multidimensional Poverty Measurement," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-58235-4.
    7. Lin Yang, 2018. "Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 19(4), pages 456-476, October.
    8. Ricardo Fraiman & Graciela Muniz, 2001. "Trimmed means for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(2), pages 419-440, December.
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