PUBLISHED PAPERS (REFEREED)
2020
- Mulder, J., Gu, Olsson-Collentine, Böing-Messing, Meijerink, Williams, Hoijtink, Fox, Wagenmakers, Rosseel, Menke, Tomarken, van Lissa (accepted). BFpack: Flexible Bayes factor testing of scientific theories in R. Journal of Statistical Software. (preprint)
- Mulder, J., Berger, J.O., Peña, V., & Bayarri, M.J. (2020). On the prevalence of information inconsistency in normal linear models. TEST. (paper)
- Mulder, Wagenmakers, & Marsman (2020). A generalization of the Savage-Dickey density ratio for equality and order constrained testing. The American Statistician. (paper)
- Williams, D. W. and Mulder, J. (2020). Bayesian Hypothesis Testing for Gaussian Graphical Models: Conditional Independence and Order Constraints. Journal of Mathematical Psychology, 22. (preprint)
- Williams, Mulder, Rouder, & Rast. (2020). Beneath the Surface: Unearthing Within-Person Variability and Mean Relations with Bayesian Mixed Models. Psychological Methods. (preprint).
- Williams, D. W., Rast, P., Pericchi, L. R, and Mulder, J. (2020). Comparing Gaussian Graphical Models with the Posterior Predictive Distribution and Bayesian Model Selection. Psychological Methods. (paper).
- Briganti, G., Williams, D. R., Mulder, J., Linkowski, P. (2020). Bayesian network structure and predictability of autistic traits. Psychological Reports. (paper).
- Gu, Hoijtink, & Mulder. (2020). Bayesian one-sided variable selection. Multivariate Behavioral Research. (paper).
- Kavelaars, Mulder, & Kaptein. (2020). Bayesian analysis of clinical trial designs with multiple binary endpoints. Statistical Methods in Medical Research. (paper).
- Williams & Mulder (2020). BGGM: A R Package for Bayesian Gaussian Graphical Models. Journal of Open Source Software. (paper).
- Dittrich, D., Leenders, R.Th.A.J., & Mulder, J. (2020). Network autocorrelation modeling: Bayesian techniques for estimating and testing multiple network autocorrelations. Sociological Methodology. (paper).
- van Lissa, C.J., Gu, X., Mulder, J., Rosseel, Y., van Zundert, C., & Hoijtink, H. (2020). Teacher’s Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models. Structural Equation Modeling. (paper).
2019 - Mulder, J. and Leenders, R.Th.A.J. (2019). Modeling the evolution of interaction behavior in social networks: a dynamic relational event approach for real-time analysis. Chaos, Solitons & Fractals, 119, 73-85. (paper).
- Mulder, J. and Raftery, A.E. (2019). BIC extensions for order-constrained model selection. Sociological Methods and Research. (paper).
- Mulder, J. and Olsson-Collentine, A. (2019). Simple Bayesian testing of scientific expectations in linear regression models. Behavior Research Methods, 51, 1117-1130. (paper).
- Van Erp, S., Oberski, D., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31-50. (paper).
- Gu, X., Rosseel, Y., Mulder, J., & Hoijtink, H. (2019). Bain: A program for the evaluation of inequality constrained hypotheses using Bayes factors in structural equation models. Journal of Statistical Computation and Simulation. (paper).
- Böing-Messing, F. & Mulder, J. Bayes factors for testing order constrained hypotheses on variances of dependent observations. The American Statistician. (paper).
- Meens, E.E.M., Bakx, A., Mulder, J., Denissen, J.J.A. (2019). The development and validation of an Interest and Skill inventory on Educational Choices. European Journal of Psychological Assessment. (paper).
2018 - Mulder, J. and Pericchi, L.R. (2018). The matrix-F prior for estimating and testing covariance matrices. Bayesian Analysis, 13, 1189-1210. (paper).
- Mulder, J. and Fox, J.-P. (2018). Bayes factor testing of multiple intraclass correlation coefficients. Bayesian Analysis, 14, 521-552. (paper).
- Van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23, 363-388. (paper).
- Böing-Messing, F. & Mulder, J. (2018). Automatic Bayes factors for testing equality and inequality constrained hypotheses on variances. Psychometrika, 83, 586-617. (paper)
- Hoijtink, H., Gu, X., & Mulder, J. (2018). Bayesian Evaluation of Informative Hypotheses for Multiple Populations. British Journal of Mathematical and Statistical Psychology. (paper).
- Hoijtink, H., Gu, X., Mulder, J., and Rosseel, Y. (2018). Computing Bayes factors from data with missing values. Psychological Methods. (paper).
- Hoijtink, H., Mulder, J., van Lissa, C.J., & Gu, X. (2018). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods. (paper).
- Flore, P. C., Mulder, J., and Wicherts, J. (2018). The influence of gender stereotype threat on mathematics test scores of Dutch high school students: A registered report. Comprehensive Results in Social Psychology. (paper).
2017 - Gu, X., Mulder, J. & Hoijtink, H. (2017). Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses. British Journal of Mathematical and Statistical Psychology. (paper).
- Fox, J.-P., Mulder, J., & Sinharay, S. (2017). Bayes factor covariance testing in item response models. Psychometrika, 82, 976-1006. (paper)
- Dittrich, D., Leenders, R., & Mulder, J. (2017). Bayesian estimation of the network autocorrelation model. Social Networks, 48, 213-246. (paper).
- Dittrich, D., Leenders, R., & Mulder, J. (2017). Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses. Sociological Methods & Research. (paper).
- Böing-Messing, F., Van Assen, M., Hoijtink, H., Hoffman, A., & Mulder, J. (2017). Bayesian evaluation of equality and inequality constrained hypotheses on variances. Psychological Methods, 22, 262-287. (paper).
- De Jong, J., Rigotti, T., & Mulder, J. (2017). One after the other: Effects of sequence patterns of breaches and overfulfilled obligations. European Journal of Work and Organizational Psychology, 26, 337-355. (paper).
- Kollenburg, G., Mulder, J., & Vermunt, J.K. (2017). Posterior calibration of posterior predictive p-values. Psychological Methods, 22, 382-396. (paper).
2016 - Mulder J. (2016). Bayes Factors for Testing Order-Constrained Hypotheses on Correlations. Journal of Mathematical Psychology, 72, 104-115. (paper).
- Mulder, J. & Wagenmakers, E.-J. (2016). Editors’ Introduction to the Special Issue “Bayes Factors for Testing Hypotheses in Psychological Research: Practical Relevance and New Developments”. Journal of Mathematical Psychology, 72, 1-5. (paper).
- Fox, J.-P., Marsman, M., Mulder, J., & Verhagen, J. (2016). Complex latent variable modeling in educational assessment. Communications in Statistics, 45, 1499-1510. (paper).
- Böing-Messing, F. & Mulder J. (2016). Automatic Bayes Factors for Testing Variances of Two Independent Normal Distributions. Journal of Mathematical Psychology, 72, 158-170. (paper).
- Gu, X., Hoijtink, H., & Mulder, J. (2016). Error probabilities in default Bayesian hypothesis testing. Journal of Mathematical Psychology, 72, 130-143. (paper).
2015 - Braeken, J., Mulder, J., & Wood, S. (2015). Relative effects at work: Bayes factors for order hypotheses. Journal of Management, 41, 544-573. (paper).
- Van Kollenburg, G., Mulder, J., & Vermunt, J. K. (2015). Assessing model fit when asymptotics do not hold. Methodology, 11, 65-79. (paper).
2014 - Mulder, J. (2014). Bayes factors for testing inequality constrained hypotheses: Issues with prior specification. British Journal of Mathematical and Statistical Psychology, 67, 153-171.
- Mulder, J. (2014). Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses. Computational Statistics and Data Analysis, 71, 448-463.
- Gu, X., Mulder, J., Dekovic, M., & Hoijtink, H. (2014). Bayesian evaluation of inequality constrained hypotheses. Psychological Methods, 19, 511-527.
2013 - Mulder, J. & Fox, J.-P. (2013). Bayesian tests for variance components in a compound symmetry covariance structure. Statistics and Computing, 23, 109-122.
2012 - Mulder, J., Hoijtink, H., & de Leeuw, C. (2012). BIEMS: A Fortran 90 program for calculating Bayes factors for inequality and equality constrained models. Journal of Statistical Software, 46(2).
- Kluytmans, A., Van de Schoot, R., Mulder, J., & Hoijtink, H. (2012). Illustrating Bayesian evaluation of informative hypotheses for regression models. Frontiers in Psychology, 3(2).
2011 - Van de Schoot, R., Mulder, J., Hoijtink, H., van Aken, M. A. G., Semon Dubas, J., Orobio de Castro, B., Meeuw, W., & Romeijn, J. -W. (2011). An introduction to Bayesian model selection for evaluating informative hypotheses. European Journal of Developmental Psychology, 8(6), 713-729.
- Van de Schoot, R., Hoijtink, H., Mulder, J., Aken, M. V., de Castro, B. O., Meeus, W., & Romeijn, J.-W (2011). Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection. Developmental Psychology, 47 (1), 203-212.
2010 - Mulder, J., Hoijtink, H., & Klugkist, (2010). Equality and inequality constrained multivariate linear models: Objective model selection using constrained posterior priors. Journal of Statistical Planning and Inference, 140, 887-906.
2009 - Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with optimal design criterion for item selection. Psychometrika, 74, 273-296.
- Mulder, J., Klugkist, I., Meeus, W., van de Schoot, A., Selfhout, M., & Hoijtink,H. (2009). Bayesian model selection of informative hypotheses for repeated measurements. Journal of Mathematical Psychology, 53, 530-546.
- Kammers, M. P. M., Mulder, J., De Vignemont, F., & Dijkerman, H. C. (2009). The weight of representing the body: A dynamic approach to investigating multiple body representations in healthy individuals. Experimental Brain Research, 204, 333-342.
- Almond, R. G., Mulder, J., Hemat, L. A., & Yan, D. (2009). Bayesian network models for local dependence among observable outcome variables. Journal of Educational and Behavioral Statistics, 34, 491-521.
BOOKS, OR CONTRIBUTIONS TO BOOKS
- Schouten, G., Arena, G., van Leeuwen, F.C.A., Heck, P., Mulder, J., Aalbers, R., Leenders, R.Th.A.J., and Böing-Messing, F (accepted). Data science in action. In Data Science for Entrepreneurship. (van den Heuvel, van de Born, & Liebregts, Eds.).
- Mulder, J. (2016). Bayesian Testing of Constrained Hypotheses. In J. Robertson & M.C. Kaptein (Eds.), Modern Statistical Methods for HCI. Springer-Verlag.
- Mulder, J. (2010). Bayesian Model Selection for Constrained Multivariate Normal Linear Models. PhD thesis, Utrecht University.
- Mulder, J. & van der Linden, W. J. (2009).Multidimensional adaptive testing with Kullback-Leibler information item selection. In W. J. van der Linden & C. A. W. Glas (Eds.), Elements of Adaptive Testing (pp. 79-104). New York: Springer.
- Klugkist, & Mulder, J. (2008). Bayesian estimation for inequality constrained analysis of variance. In: H. Hoijtink, I. Klugkist, and P. A. Boelen. (Eds.), Bayesian Evaluation of Informative Hypotheses (pp. 27-52). New York: Springer.