Cargando…
Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach
The present article proposes and evaluates marginal maximum likelihood (ML) estimation methods for hierarchical multinomial processing tree (MPT) models with random and fixed effects. We assume that an identifiable MPT model with S parameters holds for each participant. Of these S parameters, R para...
Autores principales: | Nestler, Steffen, Erdfelder, Edgar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444666/ https://www.ncbi.nlm.nih.gov/pubmed/37247167 http://dx.doi.org/10.1007/s11336-023-09921-w |
Ejemplares similares
-
Maximum likelihood estimation of a social relations structural equation model
por: Nestler, Steffen, et al.
Publicado: (2020) -
Testing Interactions in Multinomial Processing Tree Models
por: Kuhlmann, Beatrice G., et al.
Publicado: (2019) -
A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation
por: Nestler, Steffen, et al.
Publicado: (2021) -
The impact of subjective recognition experiences on recognition heuristic use: A multinomial processing tree approach
por: Castela, Marta, et al.
Publicado: (2014) -
Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables
por: van der Ark, L. Andries, et al.
Publicado: (2023)