arXiv subjects

Fabio Vieira

Publications and source records attributed to Fabio Vieira.

3 recordsLinked to original sources

arXiv2025

To Vary or Not To Vary: A Flexible Empirical Bayes Factor for Testing Variance Components

Random effects are the gold standard for capturing structural heterogeneity in data, such as spatial dependencies, individual differences, or temporal dependencies. However, testing for their presence is challenging, as it involves a variance component constrained to be non-negative -- a boundary problem. This paper proposes a flexible empirical Bayes factor (EBF) for testing random effects. Rather than testing whether a variance component is zero, the EBF tests the equivalent hypothesis that all random effects are zero. Crucially, it avoids manual prior specification based on external knowledge, as the distribution of random effects is part of the model's lower level and estimated from the data -- yielding an "empirical" Bayes factor. The EBF uses a Savage-Dickey density ratio, allowing all random effects to be tested using only the full model fit. This eliminates the need to fit multiple models with different combinations of random effects. Simulations on synthetic data evaluate the criterion's general behavior. To demonstrate its flexibility, the EBF is applied to generalized linear crossed mixed models, spatial random effects models, dynamic structural equation models, random intercept cross-lagged panel models, and nonlinear mixed effects models.

stat.ME↗

arXiv2024

To Vary or Not To Vary: A Simple Empirical Bayes Factor for Testing Variance Components

Random effects are a flexible addition to statistical models to capture structural heterogeneity in the data, such as spatial dependencies, individual differences, temporal dependencies, or non-linear effects. Testing for the presence (or absence) of random effects is an important but challenging endeavor however, as testing a variance component, which must be non-negative, is a boundary problem. Various methods exist which have potential shortcomings or limitations. As a flexible alternative, we propose a flexible empirical Bayes factor (EBF) for testing for the presence of random effects. Rather than testing whether a variance component equals zero or not, the proposed EBF tests the equivalent assumption of whether all random effects are zero. The Bayes factor is `empirical' because the distribution of the random effects on the lower level, which serves as a prior, is estimated from the data as it is part of the model. Empirical Bayes factors can be computed using the output from classical (MLE) or Bayesian (MCMC) approaches. Analyses on synthetic data were carried out to assess the general behavior of the criterion. To illustrate the methodology, the EBF is used for testing random effects under various models including logistic crossed mixed effects models, spatial random effects models, dynamic structural equation models, random intercept cross-lagged panel models, and nonlinear regression models.

stat.ME↗

arXiv2023

A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures

Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks, while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.

stat.ME↗