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Extended Generalized Sinh-Normal Distribution

Author

Listed:
  • Guillermo Martínez-Flórez

    (Departamento de Matemática y Estadística, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 230001, Colombia
    Present address: Programa de Pós-Graduação em Modelagem e Métodos Quantitativos, Universidade Federal do Ceará, Fortaleza 60020-181, Brazil.)

  • David Elal-Olivero

    (Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1530000, Chile)

  • Carlos Barrera-Causil

    (Grupo de Investigación Davinci, Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia)

Abstract

Positively skewed data sets are common in different areas, and data sets such as material fatigue, reaction time, neuronal reaction time, agricultural engineering, and spatial data, among others, need to be fitted according to their features and maintain a good quality of fit. Skewness and bimodality are two of the features that data sets like this could present simultaneously. So, flexible statistical models should be proposed in this sense. In this paper, a general extended class of the sinh-normal distribution is presented. Additionally, the asymmetric distribution family is extended, and as a natural extension of this model, the extended Birnbaum–Saunders distribution is studied as well. The proposed model presents a better goodness of fit compared to the other studied models.

Suggested Citation

  • Guillermo Martínez-Flórez & David Elal-Olivero & Carlos Barrera-Causil, 2021. "Extended Generalized Sinh-Normal Distribution," Mathematics, MDPI, vol. 9(21), pages 1-24, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2793-:d:671843
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    References listed on IDEAS

    as
    1. Neveka M. Olmos & Guillermo Martínez-Flórez & Heleno Bolfarine, 2017. "Bimodal Birnbaum–Saunders distribution with applications to non negative measurements," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(13), pages 6240-6257, July.
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