17 citations · 87 across the 28 of their papers we have counts for
24 papers · 1 filter
Bayes Factor Testing of Linear and Nonlinear Effects in Additive Gaussian Process Regression
Yueheng Wu, Joran Jongerling, Joris Mulder
Gaussian process regression flexibly models nonlinear relationships between predictors and an outcome variable. A central question in this modeling framework is whether each predic…
Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM
Joris Mulder
Vogels et al. (2024) presented an empirical comparison of Bayesian methods for structure learning in undirected Gaussian graphical models. The method implemented in the R package B…
Bayes Factor Hypothesis Testing in Meta-Analyses: Practical Advantages and Methodological Considerations
Joris Mulder, Robbie C. M. van Aert
Bayesian hypothesis testing via Bayes factors offers a principled alternative to classical p-value methods in meta-analysis, particularly suited to its cumulative and sequential na…
Invited Discussion of "Model Uncertainty and Missing Data: An Objective Bayesian Perspective" by Gonzalo García-Donato , María Eugenia Castellanos , Stefano Cabras Alicia Quirós , and Anabel Forte
Merlise A Clyde
The article by Garc{í}a-Donato and co-authors addresses the dual challenges of accounting for model uncertainty and missing data within the Gaussian regression frameworks from an o…
Comment on García-Donato et al. (2025) "Model uncertainty and missing data: An objective Bayesian perspective"
Joris Mulder
Garcia-Donato et al. (2025) present a methodology for handling missing data in a model selection problem using an objective Bayesian approach. The current comment discusses an alte…
To Vary or Not To Vary: A Flexible Empirical Bayes Factor for Testing Variance Components
Fabio Vieira, Hongwei Zhao, Joris Mulder
Random effects are the gold standard for capturing structural heterogeneity in data, such as spatial dependencies, individual differences, or temporal dependencies. However, testin…
To Vary or Not To Vary: A Simple Empirical Bayes Factor for Testing Variance Components
Fabio Vieira, Hongwei Zhao, Joris Mulder
Random effects are a flexible addition to statistical models to capture structural heterogeneity in the data, such as spatial dependencies, individual differences, temporal depende…
Comment on "Safe Testing" by Grünwald, de Heide, and Koolen
Joris Mulder
This comment briefly reflects on "Safe Testing" by Grüwald et al. (2024). The safety of fractional Bayes factors (O'Hagan, 1995) is illustrated and compared to (safe) Bayes factors…
Fast Meta-Analytic Approximations for Relational Event Models: Applications to Data Streams and Multilevel Data
Fabio Vieira Roger Leenders Joris Mulder
Large relational-event history data stemming from large networks are becoming increasingly available due to recent technological developments (e.g. digital communication, online da…
Bayesian Testing of Scientific Expectations Under Exponential Random Graph Models
Joris Mulder, Nial Friel, Philip Leifeld
The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theo…
Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity
Xynthia Kavelaars, Joris Mulder, Maurits Kaptein
In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected…
Bayesian multivariate logistic regression for superiority and inferiority decision-making under observable treatment heterogeneity
Xynthia Kavelaars, Joris Mulder, Maurits Kaptein
The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specifi…
A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures
Fabio Vieira, Roger Leenders, Daniel McFarland +1
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 s…
Separating the Wheat from the Chaff: Bayesian Regularization in Dynamic Social Networks
Diana Karimova, Joris Mulder, Roger Th. A. J. Leenders
In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an "eve…
Bayesian testing of linear versus nonlinear effects using Gaussian process priors
Joris Mulder
A Bayes factor is proposed for testing whether the effect of a key predictor variable on the dependent variable is linear or nonlinear, possibly while controlling for certain covar…
A Bayesian semi-parametric approach for modeling memory decay in dynamic social networks
Giuseppe Arena, Joris Mulder, Roger Th. A. J. Leenders
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the quantity…
A Latent Variable Model for Relational Events with Multiple Receivers
Joris Mulder, Peter D. Hoff
Directional relational event data, such as email data, often contain unicast messages (i.e., messages of one sender towards one receiver) and multicast messages (i.e., messages of…
A Generalization of the Savage-Dickey Density Ratio for Testing Equality and Order Constrained Hypotheses
J. Mulder, E. -J. Wagenmakers, M. Marsman
The Savage-Dickey density ratio is a specific expression of the Bayes factor when testing a precise (equality constrained) hypothesis against an unrestricted alternative. The expre…
Decision-making with multiple correlated binary outcomes in clinical trials
X. M. Kavelaars, J. Mulder, M. C. Kaptein
Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to c…
Default Bayesian Model Selection of Constrained Multivariate Normal Linear Models
J. Mulder, H. Hoijtink, X. Gu
The multivariate normal linear model is one of the most widely employed models for statistical inference in applied research. Special cases include (multivariate) t testing, (M)AN(…
Simple Bayesian testing of scientific expectations in linear regression models
Joris Mulder, Anton Olsson-Collentine
Scientific theories can often be formulated using equality and order constraints on the relative effects in a linear regression model. For example, it may be expected that the effe…
Bayes factor testing of equality and order constraints on measures of association in social research
Joris Mulder, John P. T. M. Gelissen
Measures of association play a central role in the social sciences to quantify the strength of a linear relationship between the variables of interest. In many applications researc…
BIC extensions for order-constrained model selection
Joris Mulder, Adrian E. Raftery
The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC however is not suitable for evalua…
The Lazy Bootstrap. A Fast Resampling Method for Evaluating Latent Class Model Fit
Geert H. van Kollenburg, Joris Mulder, Jeroen K. Vermunt
The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional m…