Joris Mulder

3.2k total citations
79 papers, 1.7k citations indexed

About

Joris Mulder is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Joris Mulder has authored 79 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Statistics and Probability, 18 papers in Management Science and Operations Research and 15 papers in Artificial Intelligence. Recurrent topics in Joris Mulder's work include Statistical Methods and Bayesian Inference (33 papers), Advanced Statistical Methods and Models (23 papers) and Mental Health Research Topics (12 papers). Joris Mulder is often cited by papers focused on Statistical Methods and Bayesian Inference (33 papers), Advanced Statistical Methods and Models (23 papers) and Mental Health Research Topics (12 papers). Joris Mulder collaborates with scholars based in Netherlands, United States and China. Joris Mulder's co-authors include Herbert Hoijtink, Xin Gu, Sara van Erp, Daniel L. Oberski, Donald R. Williams, Wim J. van der Linden, Roger Leenders, Caspar J. Van Lissa, Irene Klugkist and Eric‐Jan Wagenmakers and has published in prestigious journals such as PLoS ONE, Journal of Management and Developmental Psychology.

In The Last Decade

Joris Mulder

73 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Joris Mulder Netherlands 24 589 403 285 257 233 79 1.7k
Sy‐Miin Chow United States 24 339 0.6× 713 1.8× 145 0.5× 201 0.8× 187 0.8× 96 1.8k
Jeffrey R. Harring United States 26 488 0.8× 376 0.9× 230 0.8× 209 0.8× 162 0.7× 84 2.0k
Willem J. Heiser Netherlands 31 345 0.6× 426 1.1× 174 0.6× 355 1.4× 176 0.8× 86 2.6k
Gunter Maris Netherlands 18 191 0.3× 398 1.0× 312 1.1× 203 0.8× 238 1.0× 57 1.1k
Herbert Hoijtink Netherlands 32 1.1k 1.9× 529 1.3× 624 2.2× 432 1.7× 358 1.5× 121 3.3k
Johan H. L. Oud Netherlands 22 229 0.4× 578 1.4× 181 0.6× 90 0.4× 109 0.5× 65 1.6k
Irene Klugkist Netherlands 22 443 0.8× 334 0.8× 157 0.6× 190 0.7× 315 1.4× 64 1.6k
Dylan Molenaar Netherlands 23 315 0.5× 668 1.7× 479 1.7× 172 0.7× 302 1.3× 76 2.0k
Wolf Vanpaemel Belgium 25 226 0.4× 701 1.7× 162 0.6× 484 1.9× 691 3.0× 68 2.6k
Joachim Vandekerckhove United States 33 377 0.6× 949 2.4× 205 0.7× 402 1.6× 1.7k 7.1× 86 3.3k

Countries citing papers authored by Joris Mulder

Since Specialization
Citations

This map shows the geographic impact of Joris Mulder's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joris Mulder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joris Mulder more than expected).

Fields of papers citing papers by Joris Mulder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joris Mulder. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joris Mulder. The network helps show where Joris Mulder may publish in the future.

Co-authorship network of co-authors of Joris Mulder

This figure shows the co-authorship network connecting the top 25 collaborators of Joris Mulder. A scholar is included among the top collaborators of Joris Mulder based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joris Mulder. Joris Mulder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mulder, Joris & Robbie C. M. van Aert. (2025). Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations. Research Synthesis Methods. 1–35.
2.
Mulder, Joris & Peter D. Hoff. (2024). A latent variable approach for modeling relational data with multiple receivers. The Annals of Applied Statistics. 18(3).
3.
Leenders, Roger, et al.. (2023). A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures. Behaviormetrika. 51(1). 37–74. 3 indexed citations
4.
Gravel, Jason, Matthew Valasik, Joris Mulder, et al.. (2023). Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models. Network Science. 11(2). 324–350. 8 indexed citations
5.
Mulder, Joris, et al.. (2023). Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity. BMC Medical Research Methodology. 23(1). 220–220. 6 indexed citations
6.
Mulder, Joris. (2022). Bayesian Testing of Linear Versus Nonlinear Effects Using Gaussian Process Priors. The American Statistician. 77(1). 1–11. 1 indexed citations
7.
Mulder, Joris & Xin Gu. (2021). Bayesian Testing of Scientific Expectations under Multivariate Normal Linear Models. Multivariate Behavioral Research. 57(5). 767–783. 6 indexed citations
8.
Leenders, Roger, et al.. (2021). Bayesian analysis of higher-order network autocorrelation models. Sociological Methodology.
9.
Mulder, Joris, Eric‐Jan Wagenmakers, & Maarten Marsman. (2020). A Generalization of the Savage–Dickey Density Ratio for Testing Equality and Order Constrained Hypotheses. The American Statistician. 76(2). 102–109. 6 indexed citations
10.
Williams, Donald R., Philippe Rast, Luis R. Pericchi, & Joris Mulder. (2020). Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection.. Psychological Methods. 25(5). 653–672. 51 indexed citations
11.
Hoijtink, Herbert, Joris Mulder, Caspar J. Van Lissa, & Xin Gu. (2019). A tutorial on testing hypotheses using the Bayes factor.. Psychological Methods. 24(5). 539–556. 139 indexed citations
12.
Mulder, Joris & Anton Olsson-Collentine. (2019). Simple Bayesian testing of scientific expectations in linear regression models. Behavior Research Methods. 51(3). 1117–1130. 7 indexed citations
13.
Mulder, Joris & Roger Leenders. (2018). Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis. Chaos Solitons & Fractals. 119. 73–85. 27 indexed citations
14.
Hoijtink, Herbert, Xin Gu, Joris Mulder, & Yves Rosseel. (2018). Computing Bayes factors from data with missing values.. Psychological Methods. 24(2). 253–268. 17 indexed citations
15.
Kollenburg, Geert van, Joris Mulder, & Jeroen K. Vermunt. (2017). Posterior calibration of posterior predictive p values.. Psychological Methods. 22(2). 382–396. 10 indexed citations
16.
Erp, Sara van, Joris Mulder, & Daniel L. Oberski. (2017). Prior sensitivity analysis in default Bayesian structural equation modeling.. Psychological Methods. 23(2). 363–388. 83 indexed citations
17.
Kluytmans, Anouck, Rens van de Schoot, Joris Mulder, & Herbert Hoijtink. (2012). Illustrating Bayesian Evaluation of Informative Hypotheses for Regression Models. Frontiers in Psychology. 3. 2–2. 22 indexed citations
18.
Schoot, Rens van de, Joris Mulder, Herbert Hoijtink, et al.. (2011). An introduction to Bayesian model selection for evaluating informative hypotheses. European Journal of Developmental Psychology. 8(6). 713–729. 19 indexed citations
19.
Schoot, Rens van de, Herbert Hoijtink, Joris Mulder, et al.. (2011). Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection.. Developmental Psychology. 47(1). 203–212. 37 indexed citations
20.
Almond, Russell G., et al.. (2006). Bayesian Network Models for Local Dependence among Observable Outcome Variables. Research Report. ETS RR-06-36.. ETS Research Report Series. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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