Modelo de regresión Birnbaum-Saunders en la presencia deefectos no param ́etricos y censura no informativa

• Laura Marcela Rúa Yánez ^[1] ; Luis Hernando Vanegas Penagos ^[2]
  1. [1] Universidad Manuela Beltrán

     Universidad Manuela Beltrán

     Colombia

     
  2. [2] Universidad Nacional de Colombia

     Universidad Nacional de Colombia

     Colombia

     
• Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 15, Nº. 2, 2022, págs. 1-15
• Idioma: español
• Títulos paralelos:
  • Birnbaum-Saunders regression model under the presence of non-parametric effectsand non-informative
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    El principal objetivo de este trabajo es proporcionar una versi ́on m ́as flexible del modelo de regresi ́onLog-Birnbaum-Saunders bajo la presencia de censura aleatoria y no informativa, donde se asume quelos efectos de algunas variables explicativas son no lineales pero su forma funcional es desconocida.La flexibilidad que proporciona el modelo que se propone radica en su capacidad para describir lamediana de la distribuci ́on del tiempo de vida mediante un conjunto de covariables haciendo uso deuna suma de funciones arbitrarias, cuya forma funcional es estimada de los datos (observaciones),reduciendo as ́ı la posibilidad de error de especificaci ́on, lo que permite estimar los par ́ametros deforma robusta bajo la presencia de extremos o outliers, incluyendo observaciones censuradas noinformativas.

  • English

    The main objective of this work is to provide a more flexible version of the Log-Birnbaum-Saundersregression model under the presence of random and non-informative censoring, where it is assumedthat the effects of some explanatory variables are nonlinear but their functional form is unknown.The flexibility provided by the proposed model lies in its ability to describe the median life-timedistribution by a set of covariates that make use of a sum of arbitrary functions, which is estimatedfrom the data (observations ), thus reducing the possibility of specification error, which allows theestimation of robust parameters under the presence of extremes values or outliers, including non-informative censored observations.

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