Publications

PUBLISHED PAPERS (REFEREED)

2020

  1. 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)
  2. Mulder, J., Berger, J.O., Peña, V., & Bayarri, M.J. (2020). On the prevalence of information inconsistency in normal linear models. TEST. (paper)
  3. Mulder, Wagenmakers, & Marsman (2020). A generalization of the Savage-Dickey density ratio for equality and order constrained testing. The American Statistician. (paper)
  4. 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)
  5. Williams, Mulder, Rouder, & Rast. (2020). Beneath the Surface: Unearthing Within-Person Variability and Mean Relations with Bayesian Mixed Models. Psychological Methods. (preprint).
  6. 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).
  7. Briganti, G., Williams, D. R., Mulder, J., Linkowski, P. (2020). Bayesian network structure and predictability of autistic traits. Psychological Reports. (paper).
  8. Gu, Hoijtink, & Mulder. (2020). Bayesian one-sided variable selection. Multivariate Behavioral Research. (paper).
  9. Kavelaars, Mulder, & Kaptein. (2020). Bayesian analysis of clinical trial designs with multiple binary endpoints. Statistical Methods in Medical Research. (paper).
  10. Williams & Mulder (2020). BGGM: A R Package for Bayesian Gaussian Graphical Models. Journal of Open Source Software. (paper).
  11. 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).
  12. 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

  13. 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).
  14. Mulder, J. and Raftery, A.E. (2019). BIC extensions for order-constrained model selection. Sociological Methods and Research. (paper).
  15. Mulder, J. and Olsson-Collentine, A. (2019). Simple Bayesian testing of scientific expectations in linear regression models. Behavior Research Methods, 51, 1117-1130. (paper).
  16. Van Erp, S., Oberski, D., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31-50. (paper).
  17. 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).
  18. Böing-Messing, F. & Mulder, J. Bayes factors for testing order constrained hypotheses on variances of dependent observations. The American Statistician. (paper).
  19. 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

  20. Mulder, J. and Pericchi, L.R. (2018). The matrix-F prior for estimating and testing covariance matrices. Bayesian Analysis, 13, 1189-1210. (paper).
  21. Mulder, J. and Fox, J.-P. (2018). Bayes factor testing of multiple intraclass correlation coefficients. Bayesian Analysis, 14, 521-552. (paper).
  22. Van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23, 363-388. (paper).
  23. Böing-Messing, F. & Mulder, J. (2018). Automatic Bayes factors for testing equality and inequality constrained hypotheses on variances. Psychometrika, 83, 586-617. (paper)
  24. Hoijtink, H., Gu, X., & Mulder, J. (2018). Bayesian Evaluation of Informative Hypotheses for Multiple Populations. British Journal of Mathematical and Statistical Psychology. (paper).
  25. Hoijtink, H., Gu, X., Mulder, J., and Rosseel, Y. (2018). Computing Bayes factors from data with missing values. Psychological Methods. (paper).
  26. Hoijtink, H., Mulder, J., van Lissa, C.J., & Gu, X. (2018). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods. (paper).
  27. 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

  28. 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).
  29. Fox, J.-P., Mulder, J., & Sinharay, S. (2017). Bayes factor covariance testing in item response models. Psychometrika, 82, 976-1006. (paper)
  30. Dittrich, D., Leenders, R., & Mulder, J. (2017). Bayesian estimation of the network autocorrelation model. Social Networks, 48, 213-246. (paper).
  31. 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).
  32. 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).
  33. 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).
  34. Kollenburg, G., Mulder, J., & Vermunt, J.K. (2017). Posterior calibration of posterior predictive p-values. Psychological Methods, 22, 382-396. (paper).

    2016
  35. Mulder J. (2016). Bayes Factors for Testing Order-Constrained Hypotheses on Correlations. Journal of Mathematical Psychology, 72, 104-115. (paper).
  36. 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).
  37. Fox, J.-P., Marsman, M., Mulder, J., & Verhagen, J. (2016). Complex latent variable modeling in educational assessment. Communications in Statistics, 45, 1499-1510. (paper).
  38. 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).
  39. Gu, X., Hoijtink, H., & Mulder, J. (2016). Error probabilities in default Bayesian hypothesis testing. Journal of Mathematical Psychology, 72, 130-143. (paper).

    2015

  40. Braeken, J., Mulder, J., & Wood, S. (2015). Relative effects at work: Bayes factors for order hypotheses. Journal of Management, 41, 544-573. (paper).
  41. Van Kollenburg, G., Mulder, J., & Vermunt, J. K. (2015). Assessing model fit when asymptotics do not hold. Methodology, 11, 65-79. (paper).

    2014


  42. Mulder, J. (2014). Bayes factors for testing inequality constrained hypotheses: Issues with prior specification. British Journal of Mathematical and Statistical Psychology, 67, 153-171.
  43. Mulder, J. (2014). Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses. Computational Statistics and Data Analysis, 71, 448-463.
  44. Gu, X., Mulder, J., Dekovic, M., & Hoijtink, H. (2014). Bayesian evaluation of inequality constrained hypotheses. Psychological Methods, 19, 511-527.

    2013

  45. Mulder, J. & Fox, J.-P. (2013). Bayesian tests for variance components in a compound symmetry covariance structure. Statistics and Computing, 23, 109-122.

    2012

  46. 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).
  47. 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

  48. 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.
  49. 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

  50. 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

  51. Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with optimal design criterion for item selection. Psychometrika, 74, 273-296.
  52. 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.
  53. 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.
  54. 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

  1. 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.).
  2. Mulder, J. (2016). Bayesian Testing of Constrained Hypotheses. In J. Robertson & M.C. Kaptein (Eds.), Modern Statistical Methods for HCI. Springer-Verlag.
  3. Mulder, J. (2010). Bayesian Model Selection for Constrained Multivariate Normal Linear Models. PhD thesis, Utrecht University.
  4. 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.
  5. 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.