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Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective

Author

Listed:
  • Cristina Davino

    (University of Naples Federico II)

  • Pasquale Dolce

    (University of Naples Federico II)

  • Stefania Taralli

    (ISTAT)

  • Domenico Vistocco

    (University of Naples Federico II)

Abstract

Quantile composite-based path modeling is a recent extension to the conventional partial least squares path modeling. It estimates the effects that predictors exert on the whole conditional distributions of the outcomes involved in path models and provides a comprehensive view on the structure of the relationships among the variables. This method can also be used in a predictive way as it estimates model parameters for each quantile of interest and provides conditional quantile predictions for the manifest variables of the outcome blocks. Quantile composite-based path modeling is shown in action on real data concerning well-being indicators. Health outcomes are assessed taking into account the effects of Economic well-being and Education. In fact, to support an accurate evaluation of the regional performances, the conditions within the outcomes arise should be properly considered. Assessing health inequalities in this multidimensional perspective can highlight the unobserved heterogeneity and contribute to advances in knowledge about the dynamics producing the well-being outcomes at local level.

Suggested Citation

  • Cristina Davino & Pasquale Dolce & Stefania Taralli & Domenico Vistocco, 2022. "Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 907-936, June.
    • Handle: RePEc:spr:soinre:v:161:y:2022:i:2:d:10.1007_s11205-020-02425-5
    DOI: 10.1007/s11205-020-02425-5
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    3. repec:dau:papers:123456789/10510 is not listed on IDEAS
    4. Cristina Davino & Vincenzo Esposito Vinzi, 2016. "Quantile composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 491-520, December.
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    6. Michel Tenenhaus, 2011. "Regularized generalized canonical correlation analysis," Post-Print hal-00578321, HAL.
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    12. Cristina Davino & Pasquale Dolce & Stefania Taralli & Vincenzo Esposito Vinzi, 2018. "A Quantile Composite-Indicator Approach for the Measurement of Equitable and Sustainable Well-Being: A Case Study of the Italian Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 999-1029, April.
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