Year: 2018
Type: preprint
Abstract: The standard procedures for analysing hierarquical or grouped data are by (non)linear mixed models or generalized mixed models. However, the generalized additive models for location, scale and shape (GAMLSSs) also allow different types of random effects to be included in the model formulation. Even though already popular in many areas of research, this type of models have not been found to be used for mixed modeling purposes yet. Therefore, this paper describes the analysis of an experiment with plants' growth using mixed GAMLSSs, comparing it to a linear mixed model approach.
Source: arXiv (Cornell University)
Institutions: Universidade de São Paulo, Instituto Federal Goiano
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2895736713