4 papers
A robust contaminated discrete Weibull regression model for outlier-prone count data
Divan A. Burger, Janet van Niekerk, Emmanuel Lesaffre
Count data often exhibit overdispersion driven by heavy tails or excess zeros, making standard models (e.g., Poisson, negative binomial) insufficient for handling outlying observat…
Addressing outliers in mixed-effects logistic regression: a more robust modeling approach
Divan A. Burger, Sean van der Merwe, Emmanuel Lesaffre
This study introduces an outlier-robust model for analyzing hierarchically structured bounded count data within a Bayesian framework, utilizing a logistic regression approach imple…
A flexible quantile mixed-effects model for censored outcomes
Divan A. Burger, Sean van der Merwe, Emmanuel Lesaffre
We introduce a Bayesian quantile mixed-effects model for censored longitudinal outcomes based on the skew exponential power (SEP) error distribution. The SEP family separates tail…
A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Divan A. Burger, Sean van der Merwe, Emmanuel Lesaffre
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditio…