GAMLSS is preferred over quantile regression when the total outcome distribution is of interest, while quantile regression suits specific quantile regions¶
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Evidence · 1 study · theoreticalr3·q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person
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Reading the evidence codes
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q1 i? When the analytic interest is the total outcome distribution, GAMLSS should be preferred over quantile regression; quantile regression is preferred only for specific value regions of the distribution. → Wiedermann 2025
Evidence¶
Wiedermann 2025¶
Wiedermann, W., Reinke, W. M., & Herman, K. C. (2025). Distributional moderation analysis: Unpacking moderation effects in intervention research. Journal of School Psychology, 108. https://doi.org/10.1016/j.jsp.2024.101399
q1 · i? · theoretical · r3
Methodological argument comparing distribution-free models (quantile regression) with complete distribution models (GAMLSS), citing prior comparative work. The article states "GAMLSSshouldbepreferred" for the total distribution, citing advantages in model fit evaluation, accommodation of discrete and mixed distributions, and avoidance of quantile crossing. No empirical test is reported in this article for this comparison.
"overall, quantile regression should only be preferred over GAMLSS when one is interested in specific value regions (i.e., specific quantiles)oftheoutcomedistribution.Whenoneisinterestedinthetotaloutcomedistribution,GAMLSSshouldbepreferred"
Discussion¶
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