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Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution

Author

Listed:
  • Gabriela M. Rodrigues

    (Department of Exact Sciences, University of São Paulo, Piracicaba 13418-900, Brazil
    These authors contributed equally to this work.)

  • Edwin M. M. Ortega

    (Department of Exact Sciences, University of São Paulo, Piracicaba 13418-900, Brazil
    These authors contributed equally to this work.)

  • Gauss M. Cordeiro

    (Department of Statistics, Federal University of Pernambuco, Recife 50670-901, Brazil
    These authors contributed equally to this work.)

  • Roberto Vila

    (Department of Statistics, University of Brasilia, Brasilia 70910-900, Brazil
    These authors contributed equally to this work.)

Abstract

We define a new quantile regression model based on a reparameterized exponentiated odd log-logistic Weibull distribution, and obtain some of its structural properties. It includes as sub-models some known regression models that can be utilized in many areas. The maximum likelihood method is adopted to estimate the parameters, and several simulations are performed to study the finite sample properties of the maximum likelihood estimators. The applicability of the proposed regression model is well justified by means of a gastric carcinoma dataset.

Suggested Citation

  • Gabriela M. Rodrigues & Edwin M. M. Ortega & Gauss M. Cordeiro & Roberto Vila, 2023. "Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1518-:d:1103057
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    References listed on IDEAS

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    1. Mustafa Ç. Korkmaz & Christophe Chesneau & Zehra Sedef Korkmaz, 2023. "A new alternative quantile regression model for the bounded response with educational measurements applications of OECD countries," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(1), pages 131-154, January.
    2. Gijbels, Irène & Karim, Rezaul & Verhasselt, Anneleen, 2021. "Semiparametric quantile regression using family of quantile-based asymmetric densities," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Luis Sánchez & Víctor Leiva & Manuel Galea & Helton Saulo, 2020. "Birnbaum-Saunders Quantile Regression Models with Application to Spatial Data," Mathematics, MDPI, vol. 8(6), pages 1-17, June.
    4. 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.
    5. Helton Saulo & Roberto Vila & Giovanna V. Borges & Marcelo Bourguignon & Víctor Leiva & Carolina Marchant, 2023. "Modeling Income Data via New Parametric Quantile Regressions: Formulation, Computational Statistics, and Application," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    6. Roberta La Haye & Petr Zizler, 2019. "The Gini mean difference and variance," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 43-52, April.
    7. Wang, Huixia Judy & Wang, Lan, 2009. "Locally Weighted Censored Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1117-1128.
    8. De Backer, Mickael & El Ghouch, Anouar & Van Keilegom, Ingrid, 2019. "An Adapted Loss Function for Censored Quantile Regression," LIDAM Reprints ISBA 2019054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    10. De Backer, Mickaël & El Ghouch, Anouar & Van Keilegom, Ingrid, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," LIDAM Reprints ISBA 2020010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2019. "An Adapted Loss Function for Censored Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1126-1137, July.
    12. 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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    Cited by:

    1. Francisco Germán Badía & María D. Berrade, 2023. "Special Issue “Probability Theory and Stochastic Modeling with Applications”," Mathematics, MDPI, vol. 11(14), pages 1-3, July.

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