Bayesian truncated beta nonlinear mixed-effects models

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Bayesian analysis for heavy-tailed nonlinear mixed effects models

Abstract: Nonlinear models have many applications in different areas such as pharmacokinetics and pharmacodynamics, and random effects are often included to take into account the correlation between observations taken within the same subject. In this context, we propose a bayesian analysis for heavy-tailed nonlinear mixed effects models, which may produce more robust estimates for the parameter...

Bayesian Mixed-Effects Models for Recommender Systems

We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the cold-start issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results.

Model Building for Nonlinear Mixed-Effects Models

Nonlinear mixed-effects models involve both fixed effects and random effects. Model building for nonlinear mixed-effects models is the process of determining the characteristics of both the fixed and the random effects so as to give an adequate but parsimonious model. We describe procedures based on information criterion statistics for comparing different structures of the random effects compon...

Nonlinear mixed effects models for longitudinal data