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The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

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  • Freddy Hernández

    (Escuela de Estadística, Universidad Nacional de Colombia sede Medellín, Medellín 050034, Colombia)

  • Viviana Giampaoli

    (Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo 05508-090, Brazil)

Abstract

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

Suggested Citation

  • Freddy Hernández & Viviana Giampaoli, 2018. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model," Stats, MDPI, vol. 1(1), pages 1-29, May.
    • Handle: RePEc:gam:jstats:v:1:y:2018:i:1:p:5-76:d:149901
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    References listed on IDEAS

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