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A new quantile regression model with application to human development index

Author

Listed:
  • Gauss M. Cordeiro

    (Federal University of Pernambuco)

  • Gabriela M. Rodrigues

    (University of São Paulo)

  • Fábio Prataviera

    (University of São Paulo)

  • Edwin M. M. Ortega

    (University of São Paulo)

Abstract

A new odd log-logistic unit omega distribution is defined and studied, and some of its structural properties are obtained. A quantile regression model based on the new re-parameterized distribution is constructed, and the estimation is conducted by the maximum likelihood method. Monte Carlo simulations are used to assess the accuracy of the estimators. The flexibility, practical relevance and applicability of the proposed regression are proved by means of Human Development Index data from the cities of the state of São Paulo (Brazil).

Suggested Citation

  • Gauss M. Cordeiro & Gabriela M. Rodrigues & Fábio Prataviera & Edwin M. M. Ortega, 2024. "A new quantile regression model with application to human development index," Computational Statistics, Springer, vol. 39(6), pages 2925-2948, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01413-w
    DOI: 10.1007/s00180-023-01413-w
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    References listed on IDEAS

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    1. Gómez-Déniz, Emilio & Sordo, Miguel A. & Calderín-Ojeda, Enrique, 2014. "The Log–Lindley distribution as an alternative to the beta regression model with applications in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 54(C), pages 49-57.
    2. Yufeng Liu & Yichao Wu, 2011. "Simultaneous multiple non-crossing quantile regression estimation using kernel constraints," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 415-437.
    3. J. Mazucheli & A. F. B. Menezes & L. B. Fernandes & R. P. de Oliveira & M. E. Ghitany, 2020. "The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(6), pages 954-974, April.
    4. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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