A robust mixed-effects parametric quantile regression model for continuous proportions : quantifying the constraints to vitality in cushion plants

DATA AVAILABILITY STATEMENT : The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. There is no literature on outlier-robust parametric mixed-effects quantile regression mod...

Full description

Bibliographic Details
Published in:Statistica Neerlandica
Main Authors: Burger, Divan Aristo, Van der Merwe, Sean, Lesaffre, Emmanuel, Le Roux, Peter Christiaan, Raath-Kruger, Morgan J.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2023
Subjects:
Bayesian
Continuous proportions
Cushion plants
Mixed-effects
Outliers
Quantile regression
Sub-Antarctic
Antarctic
Antarc*
Online Access:http://hdl.handle.net/2263/94038
https://doi.org/10.1111/stan.12293
Description
Summary:DATA AVAILABILITY STATEMENT : The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. There is no literature on outlier-robust parametric mixed-effects quantile regression models for continuous proportion data as an alternative to systematically identifying and eliminating outliers. To fill this gap, we formulate a robust method by extending the recently proposed fixed-effects quantile regression model based on the heavy-tailed Johnson-t distribution for continuous proportion data to the mixed-effects modeling context, using a Bayesian approach. Our proposed method is motivated by and used to model the extreme quantiles of the vitality of cushion plants to provide insights into the ecology of the system in which the plants are dominant. We conducted a simulation study to assess the new method’s performance and robustness to outliers. We show that the new model has good accuracy and confidence interval coverage properties and is remarkably robust to outliers. In contrast, our study demonstrates that the current approach in the literature for modeling hierarchically structured bounded data’s quantiles is susceptible to outliers, especially when modeling the extreme quantiles. We conclude that the proposed model is an appropriate robust alternative to the cur-rent approach for modeling the quantiles of correlated continuous proportions when outliers are present in the data. https://onlinelibrary.wiley.com/journal/14679574 am2024 Plant Production and Soil Science Statistics None

Similar Items