2 papers
stat.CO2018
Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference
Daniel W. Heck, Clintin P. Davis-Stober
Many psychological theories can be operationalized as linear inequality constraints on the parameters of multinomial distributions (e.g., discrete choice analysis). These constrain…
stat.ME2018
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation
Daniel W. Heck, Morten Moshagen, Edgar Erdfelder
The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it…