Logotipo del repositorio
  • English
  • Español
  • Français
  • Português
  • Iniciar sesión
    ¿Has olvidado tu contraseña?
Logotipo del repositorio
  • Comunidades
  • Listar por
  • English
  • Español
  • Français
  • Português
  • Iniciar sesión
    ¿Has olvidado tu contraseña?
  1. Inicio
  2. Examinar por materia

Examinando por Materia "Case-wise contamination"

Mostrando 1 - 3 de 3
Resultados por página
Opciones de ordenación
  • No hay miniatura disponible
    Ítem
    Robust three-step regression based on comedian and its performance in cell-wise and case-wise outliers
    (MDPI AG, 2020-01-01) Velasco H.; Laniado H.; Toro M.; Leiva V.; Lio Y.; Velasco H.; Laniado H.; Toro M.; Leiva V.; Lio Y.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Both cell-wise and case-wise outliers may appear in a real data set at the same time. Few methods have been developed in order to deal with both types of outliers when formulating a regression model. In this work, a robust estimator is proposed based on a three-step method named 3S-regression, which uses the comedian as a highly robust scatter estimate. An intensive simulation study is conducted in order to evaluate the performance of the proposed comedian 3S-regression estimator in the presence of cell-wise and case-wise outliers. In addition, a comparison of this estimator with recently developed robust methods is carried out. The proposed method is also extended to the model with continuous and dummy covariates. Finally, a real data set is analyzed for illustration in order to show potential applications. © 2020 by the authors.
  • No hay miniatura disponible
    Ítem
    Robust three-step regression based on comedian and its performance in cell-wise and case-wise outliers
    (MDPI AG, 2020-01-01) Velasco H.; Laniado H.; Toro M.; Leiva V.; Lio Y.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Both cell-wise and case-wise outliers may appear in a real data set at the same time. Few methods have been developed in order to deal with both types of outliers when formulating a regression model. In this work, a robust estimator is proposed based on a three-step method named 3S-regression, which uses the comedian as a highly robust scatter estimate. An intensive simulation study is conducted in order to evaluate the performance of the proposed comedian 3S-regression estimator in the presence of cell-wise and case-wise outliers. In addition, a comparison of this estimator with recently developed robust methods is carried out. The proposed method is also extended to the model with continuous and dummy covariates. Finally, a real data set is analyzed for illustration in order to show potential applications. © 2020 by the authors.
  • No hay miniatura disponible
    Ítem
    Robust three-step regression based on comedian and its performance in cell-wise and case-wise outliers
    (MDPI AG, 2020-01-01) Velasco H.; Laniado H.; Toro M.; Leiva V.; Lio Y.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)
    Both cell-wise and case-wise outliers may appear in a real data set at the same time. Few methods have been developed in order to deal with both types of outliers when formulating a regression model. In this work, a robust estimator is proposed based on a three-step method named 3S-regression, which uses the comedian as a highly robust scatter estimate. An intensive simulation study is conducted in order to evaluate the performance of the proposed comedian 3S-regression estimator in the presence of cell-wise and case-wise outliers. In addition, a comparison of this estimator with recently developed robust methods is carried out. The proposed method is also extended to the model with continuous and dummy covariates. Finally, a real data set is analyzed for illustration in order to show potential applications. © 2020 by the authors.

Vigilada Mineducación

Universidad con Acreditación Institucional hasta 2026 - Resolución MEN 2158 de 2018

Software DSpace copyright © 2002-2025 LYRASIS

  • Configuración de cookies