Publications list of the research group since 1998

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International journal articles

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Editor of book

  • De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York: Springer.

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Special issues

  • Kuppens, P., Stouten, J., & Mesquita, B. (Eds.). (2009). Individual differences in emotion components and dynamics. [Special issue]. Cognition & Emotion, 23(7).
  • Van Mechelen, I., & De Raad, B. (Eds.). (1999). Personality and situations [Special issue]. European Journal of Personality, 13(5).
  • Van Mechelen, I., & Hoijtink, H. (Eds.). (2000). Special issue on Bayesian computational statistics [Special issue]. Computational Statistics, 15(3).

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Chapter in book

  • Bang-Jensen, J., Chiarandini, M., Goegebeur, Y., & Jørgensen, B. (2007). Mixed models for the analysis of local search components. In T. Stützle, M. Birattari, & H. Hoos (Eds.), Engineering stochastic local search algorithms: Designing, implementing and analyzing effective heuristics. (pp. 91-105). Berlin: Springer.
  • Ceulemans, E., & Van Mechelen, I. (2003). An algorithm for HICLAS-R models. In M. Schader, W. Gaul, & M. Vichi (Eds.), Between data science and applied data analysis: Studies in classification, data analysis, and knowledge organization (pp. 173-181). Heidelberg-Berlin: Springer Verlag GMBH.
  • Chiarandini, M., & Goegebeur, Y. (2009). Mixed models for the analysis of optimization algorithms. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, & M. Preuss (Eds.), Empirical Methods for the Analysis of Optimization Algorithms (pp. 225-264). Berlin: Springer.
  • De Bie, T., & Cristianini, N. (2006). Semi-supervised learning using semi-definite programming. In O. Chapelle, B. Schoelkopf, & A. Zien (Eds.), Semi-supervised learning (pp. 113-130). Cambridge: MIT Press.
  • De Boeck, P., & Smits, D. J. M. (2006). A double-structure structural equation model for the study of emotions and their components. In Q. Jing, M. R. Rosenzweig, G. d'Ydewalle, H. Zhang, H. Chen, & K. Zhang (Eds.), Progress in psychological science around the world. Vol. 1: Neural, cognitive and developmental issues (pp. 349-365). Hove, East Sussex: Psychology Press.
  • De Boeck, P., & Wilson, M. (2004). A framework for item response models. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 3-41). New York: Springer.
  • Depril, D., & Van Mechelen, I. (2005). One-mode additive clustering of multiway data. In J. Janssen & P. Lenca (Eds.), Applied stochastic models and data analysis (pp. 724-729). Brest, France: ENST Bretagne.
  • De Raad, B., & Ceulemans, E. (2001). The trait-dimensional scope of the characters of Theophrastus. In R. Riemann, F. M. Spinath, & F. Ostendorf (Eds.), Personality and temperament: Genetics, evolution, and structure (pp. 168-184). Lengerich, Germany: Pabst Science.
  • De Schutter, B., Schepers, J., & Van Mechelen, I. (2006). On algorithms for a binary-real (max,x) matrix approximation problem. In Proceedings of the 45th IEEE Conference on Decision & Control (pp. 5168-5173). San Diego: IEEE Control System Society.
  • Janssen, R. (2010). Using a differential item functioning approach to investigate measurement invariance. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural analysis: methods and applications (pp. 415-432). New York: Routledge Academic.
  • Janssen, R. (2010). Modeling the effect of item designs within the Rasch model. In S. Embretson (Ed.), Measuring Psychological Constructs: Advances in Model-Based Approaches (pp. 227-245). Washington, DC: American Psychological Association.
  • Janssen, R., Schepers, J., & Peres, D. (2004). Models with item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 189-212). New York: Springer.
  • Kuppens, P. (2009). Blame. In D. Sander & K. R. Scherer (Eds.), Oxford companion to emotion and the affective sciences (pp. 226-227). Oxford: Oxford University Press.
  • Kuppens, P. (2009). Towards understanding individual differences in emotional experience. In A. Przepiórka (Ed.), Closer to emotions III (pp. 73-88). Lublin: KUL.
  • Kuppens, P. (2009). Irritation. In D. Sander & K. R. Scherer (Eds.), Oxford companion to emotion and the affective sciences (pp. 77-78). Oxford: Oxford University Press.
  • Kuppens, P. (2009). Anger. In D. Sander & K. R. Scherer (Eds.), Oxford companion to emotion and the affective sciences (pp. 32-33). Oxford: Oxford University Press.
  • Leenen, I., & Van Mechelen, I. (1998). A branch-and-bound algorithm for Boolean regression. In I. Balderjahn, R. Mathar, & M. Schader (Eds.), Classification, data analysis, and data highways (pp. 164-171). Berlin: Springer.
  • Lombardi, L., Ceulemans, E., & Van Mechelen, I. (2003). A hierarchical classes approach to discriminant analysis. In M. Schader, W. Gaul, & M. Vichi (Eds.), Between data science and applied data analysis: Studies in classification, data analysis, and knowledge organization (pp. 296-304). Heidelberg-Berlin: Springer Verlag GMBH.
  • Meulders, M., De Boeck, P., & Van Mechelen, I. (2002). Rater classification on the basis of latent features in responding to situations. In W. Gaul & G. Ritter (Eds.), Classification, automation, and new media (pp. 453-461). Berlin: Springer.
  • Meulders, M., Gelman, A., Van Mechelen, I., & De Boeck, P. (1998). Generalizing the probability matrix decomposition model: An example of Bayesian model checking and model expansion. In J. Hox & E. De Leeuw (Eds.), Assumptions, robustness, and estimation methods in multivariate modeling (pp. 1-19). Amsterdam: TT Publications.
  • Meulders, M., & Xie, Y. (2004). Person-by-item predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 213-240). New York: Springer.
  • Pochet, N. L. M. M., Ojeda, F., De Smet, F., De Bie, T., Suykens, J. A. K., & De Moor, B. L. R. (2006). Kernel clustering for knowledge discovery in clinical microarray data analysis. In G. Camps-Valls, J. L. Rojo-Alvarez, & M. Martinez-Ramon (Eds.), Kernel methods in bioengineering, signal and image processing (pp. 65-93). Hershey, Pennsylvania (US): Idea Group Inc.
  • Rijmen, F., & Briggs, D. (2004). Multidimensional person variance and latent item predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 247-265). New York: Springer.
  • Rijmen, F., Tuerlinckx, F., Meulders, M., Smits, D. J. M., & Balázs, K. (2007). Mixed model estimation methods for item response models. In E. V. Smith & R. M. Smith (Eds.), Rasch measurement: Advanced and specialized applications. Maple Grove, MN: JAM Press.
  • Smits, D. J. M., & Moore, S. (2004). Latent item predictors with fixed effects. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 267-287). New York: Springer.
  • Timmerman, M. E., & Ceulemans, E. (2010). The generic subspace clustering model. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT'2010. 19th International Conference on Computational Statistics (pp. 359-366). Heidelberg: Physica-Verlag.
  • Timmerman, M. E., Ceulemans, E., Lichtwarck-Aschoff, A., & Vansteelandt, K. (2009). Multilevel component analysis for studying intra-individual variability and inter-individual differences. In J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra, & N. Chaudhary (Eds.), Dynamic process methodology in the social and developmental sciences (pp. 291-318). New York: Springer.
  • Tuerlinckx, F. (2010). Weibull distribution. In N. Salkind (Ed.), Encyclopedia of Research Design (pp. 1615-1617). Thousand Oaks, CA: Sage.
  • Tuerlinckx, F., & De Boeck, P. (2004). Models for residual dependencies. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 289-316). New York: Springer.
  • Tuerlinckx, F., Rijmen, F., Molenberghs, G., Verbeke, G., Briggs, D., Van den Noortgate, W., Meulders, M., & De Boeck, P. (2004). Estimation and software. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 343-373). New York: Springer.
  • Tuerlinckx, F., & Wang, W. C. (2004). Models for polytomous data. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 75-109). New York: Springer.
  • Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2008). A Bayesian approach to diffusion process models of decision-making. In B. Love, K. McRae, & V. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1429-1434). Austin, TX: Cognitive Science Society.
  • Van den Noortgate, W., & Paek, I. (2004). Person regression models. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 167-187). New York: Springer.
  • Van Dijk, E., De Cremer, D., Mulder, L., & Stouten, J. (2008). How do we react to feedback in social dilemmas? In A. Biel, D. Eek, T. Gärling, & M. Gustafsson (Eds.), New Issues and Paradigms in Research on Social Dilemmas (pp. 43-56). New York: Springer.
  • Van Mechelen, I., Bock, H.-H., & De Boeck, P. (2005). Two-mode clustering. In B. Everitt & D. Howell (Eds.), Encyclopedia of statistics in behavioral science (pp. 2081-2086). Chichester: Wiley.
  • Van Mechelen, I., & Leenen, I. (1998). Predicting a binary criterion variable on the basis of binary predictors: The combination rule analysis approach. In K. C. Klauer & H. Westmeyer (Eds.), Psychologische Methoden und Soziale Prozesse (pp. 136-153). Lengerich, Germany: Pabst Science.
  • Van Mechelen, I., & Van Deun, K. (2010). Multiple nested reductions of single data modes as a tool to deal with large data sets. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT'2010. 19th International Conference on Computational Statistics (pp. 349-358). Heidelberg: Physica-Verlag.
  • Van Mechelen, I., Verduyn, P., & Brans, K. (in press). The duration of emotional episodes. In D. Hermans, B. Rimé, & B. Mesquita (Eds.), Changing emotions. London: Psychology Press.
  • Voorspoels, W., Vanpaemel, W., & Storms, G. (2010). Ideal representations in a similarity space. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2290-2295). Austin, TX: Cognitive Science Society.
  • Wilson, M., & De Boeck, P. (2004). Descriptive and explanatory item response models. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 43-74). New York: Springer.

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