Books / Monographs

• Hubert, L.J., Arabie, P., & Meulman, J.J. (2006). The Structural Representation of Proximity Matrices with MATLAB. Philadelphia: ASA-SIAM.
• Meulman, J. J., & Heiser, W. J. (2005). Categories 14.0. Chicago: SPSS Inc.
• Yanai, H., Okada, A., Shigemasu, K., Kano, Y., & Meulman, J,J, (Eds.) (2003). New Developments in Psychometrics. Tokyo: Springer-Verlag.
• Hubert, L.J., Arabie, P., & Meulman, J.J. (2001). Combinatorial Data Analysis: Optimization by Dynamic Programming. Philadelphia: SIAM.
• Meulman, J. J., Heiser, W. J., & SPSS Inc. (1999). Categories 10.0. Chicago: SPSS Inc.
• Albert Gifi (1990). Nonlinear Multivariate Analysis (Eds. W.J. Heiser, J.J. Meulman, G. van der Berg). New York: Wiley.
• Coppi R., & Bolasco, S. (Eds., 1989), The analysis of multiway data matrices. Amsterdam: North-Holland [Associate Editor].
• De Leeuw, J., Heiser, W.J., Meulman, J.J., & Critchley, F. (Eds., 1986). Multidimensional data analysis. Leiden: DSWO Press.
• Meulman, J.J. (1986). A distance approach to nonlinear multivariate analysis. Leiden: DSWO Press.
• Meulman, J.J. (1982). Homogeneity analysis of incomplete data. Leiden: DSWO Press.

Journal Articles/Chapters in Books/Papers in Edited Volumes (incomplete)

• Herman A Van Wietmarschen, Weidong Dai, Anita J Van Der Kooij, Theo H Reijmers, Yan Schroë, Mei Wang, Zhiliang Xu, Xinchang Wang, Hongwei Kong, Guowang Xu, Thomas Hankemeier, Jacqueline J Meulman, Jan Van Der Greef (2012). Characterization of Rheumatoid Arthritis Subtypes Using Symptom Profiles, Clinical Chemistry and Metabolomics Measurements. PLoS ONE 09, 7(9).
• Draisma, H.H.M., Reijmers, T. H., Meulman, J.J., van der Greef, J., Hankemeier, T., Boomsma, D.I. (2012). Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families. European journal of human genetics: EJHG 06/2012.
• Linting, M,. van Os, B.J., Meulman, J.J. (2012). Statistical Significance of the Contribution of Variables to the PCA solution: An Alternative Permutation Strategy. Psychometrika 04/2012; 76(3), 440-460.
• Rippe, R.C.A., Meulman, J.J., Eilers, P.H.C. (2012). Correction of fluorescence bias on Affymetrix genotyping microarrays. Journal of Chemometrics 04/2012.
• Rippe, R.C.A., Meulman, J.J., Eilers, P.H.C. (2012). Visualization of genomic changes by segmented smoothing using an L0 penalty. PLoS ONE 01/2012; 7(6), e38230.
• Rippe, R.C.A., Meulman, J.J., Eilers, P.H.C. (2012). Reliable single chip genotyping with semi-parametric log-concave mixtures. PLoS ONE 01/2012; 7(10),e46267.
• Van Wietmarschen, H.A., Reijmers, T.H., van der Kooij, A.J., Schroën, J., Wei, H., Hankemeier, T., Meulman, J.J., van der Greef, J. (2011). Sub-typing of rheumatic diseases based on a systems diagnosis questionnaire. PLoS ONE 01/2011; 6(9), e24846.
• Draisma, H.H.M., Reijmers, T. H., van der Kloet, F., Bobeldijk-Pastorova, I., Spies-Faber, E., Vogels, J.T.W.E., Meulman, J.J., Boomsma, D.I., van der Greef, J., Hankemeier, T., (2010). Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets. Analytical Chemistry 02/2010; 82(3), 1039-46.
• Lombardo, R., Meulman, J.J. (2010). Multiple Correspondence Analysis via Polynomial Transformations of Ordered Categorical Variables. Journal of  Classification. 01/2010; 27, 191-210.
• Draisma, H.H.M., Reijmers, T. H., van der Kloet, F., Bobeldijk-Pastorova, I., Spies-Faber, E., Vogels, J.T.W.E., Meulman, J.J., van der Greef, J., Boomsma, D.I., Hankemeier, T., (2010). Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs. Omics A Journal of Integrative Biology 04/2008; 12(1), 17-31.Van der Kooij, A.J., Meulman, J.J., & Heiser, W.J. (2006).Local minima in categorical multiple regression. Computational Statistics and Data Analysis, 5, 446-462.
• Meulman, J.J., Van der Kooij,. A.J., & Heiser, W.J. (2004). Principal Components Analysis with Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data. In: D. Kaplan (ed.), Handbook of Quantitative Methods in the Social Sciences, (pp. 49-70). Newbury Park, CA: Sage Publications.
• Friedman, J.F. & Meulman, J.J. (2004). Clustering objects on subsets of variables (with discussion). Journal of the Royal Statistical Society, Series B, 4, 815-849.
• Meulman, J.J. (2003). Prediction and Classification in Nonlinear Data Analysis: Something old, something new, something borrowed, something blue. Psychometrika, 68, 493-517.
• Friedman, J.F. & Meulman, J.J. (2003). Prediction with multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 1365-1381.
• Bensmail, H. & Meulman, J.J. (2003). Model-based clustering with noise: Bayesian inference and information. Journal of Classification, 20, 49-76.
• Theunissen, N.C.M., Meulman, J.J., den-Ouden, A.L., Koopman, H.M., Verrips, G.H., Verloove-Vanhorick, S.P., & Wit, J.M. (2003). Changes can be studied when the measurement instrument is different at different time points. Health Services and Outcomes Research Methodology, 4, 109-126.
• Hubert, L. J. , Arabie, P. & Meulman, J.J. (2002). Linear Unidimensional Scaling in the L2-Norm: Basic Optimization Methods Using MATLAB. Journal of Classification, 19, 303-328.
• Meulman, J.J., Van der Kooij, A., & Babinec, A. (2002). New features of categorical principal components analysis for complicated data sets, including data mining. In: W. Gaul & G. Ritter (Eds.), Classification, data analysis, and knowledge organization, (pp. 207-217). Berlin: Springer Verlag.
• Dusseldorp, E. & Meulman, J.J. (2002). Application of data mining tools in the behavioral sciences. In: J. Meij (Ed.), Dealing with the data flood: mining data, text, and multimedia, (pp. 220-234). The Hague: STT/Beweton Netherlands Study Center for Technology Trends.
• Hubert, L.J., Arabie, P., Meulman, J.J. (2001). Dynamic programming in clustering. In: C.A. Floudas & P.M. Pardalos (Eds.), Encyclopedia of optimization, Volume 1, A-D, (pp. 506-513). Dordrecht, The Netherlands: Kluwer Academic Publishers.
• Hubert, L.J., Arabie, P., & Meulman, J.J. (2001). Modeling dissimilarity: Generalizing ultrametric and additive tree representations. British Journal of Mathematical and Statistical Psychology, 54, 103-123.
• Dusseldorp, E. & Meulman, J.J. (2001). Prediction by integration of regression trees and linear regression with optimal scaling. Methods of Information in Medicine, 40, 403-409.
• Meulman, J.J. (2000). Relational data analysis: Discrimination and classification In: W. Gaul & O. Opitz (Eds.), Classification and Information Processing at the Turn of the Millenium, (pp. 32-39). Berlin: Springer Verlag.
• Zeijl, E, te Poel, Y., du Bois-Reymond, M., Ravesloot, J., & Meulman, J.J. (2000) The role of parents and peers in the leisure activities of young adolescents, Journal of Leisure Research, 32, 281-302.
• Theunissen, N.C.M., den-Ouden, A.L., Meulman, J.J., Koopman, H.M., Verloove-Vanhorick, S.P., & Wit, J.-M. (2000). Health Status developments in a cohort of pretermchildren, The Journal of Pediatrics, 147, 534-539.
• Hubert, L.J., Meulman, J.J., & Heiser, W.J (2000). Two purposes for matrix factorization: A historical appraisal. SIAM Review, 42, 68-82.
• Haas, M. de, Algera, J.A., van Tuijl, H.F.J.M., & Meulman, J.J. (2000). Macro and microgoal setting: in search of coherence. Applied Psychology: An International Review, 49, 577-593.
• Groenen, P.J.F., Van Os, B.J., & Meulman J.J. (2000). Optimal scaling by length-constrained alternating nonnegative least squares: an application to distance-based principal coordinate analysis, Psychometrika, 65, 511-524.
• Groenen, P.J.F., Heiser W.J., & Meulman, J.J. (1999). Global optimization in least-squares multidimensional scaling by distance smoothing. Journal of Classification, 16, 225–254.
• Gower, J.C., Meulman, J.J., & Arnold, G.M. Non-metric linear biplots (1999). Journal of Classification, 16, 181-196.
• Dusseldorp, E., Van Elderen, T., Maes, S., Meulman J., & Kraaij, V. (1999). A Meta-Analysis of Psycho-Educational Programs for Coronary Heart Disease Patients. Health Psychology, 18, 506-519.
• Commandeur, J.J.F., Groenen, P.J.F., & Meulman J.J. (1999). A distance-based variety of nonlinear multivariate analysis, including weights for objects and variables. Psychometrika, 64, 169-186.
• Meulman, J.J., (1998). A distance-based biplot for multidimensional scaling of multivariate data. In: C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H.H. Bock & Y. Baba (Eds.), Data Science, Classification, and Related Methods, pp. 506-517. Tokyo: Springer Verlag.
• Meulman, J.J., Hubert, L.J. & Heiser, W.J. (1998). The Data Theory Scaling System. In: A. Rizzi, M. Vichi & H.H. Bock (Eds.), Advances in Data Science and Classification, (pp. 489-496). Berlin: Springer Verlag.
• Meulman, J.J. (1998). Book review of W.J. Krzanowski and F.H.C. Marriott, Multivariate Analysis, Part I, Distributions, Ordination and Inference, Journal of Classification, 15, 287-293.
• Meulman, J.J. (1998). Optimal scaling methods for graphical display of multivariate data. In: R. Payne & P. Green (Eds.), COMPSTAT 1998 Proceedings in Computational Statistics, (pp. 65-76). Heidelberg: Physica-Verlag.
• Hubert, L.J., Arabie, P., & Meulman, J.J. (1998) The representation of symmetric proximity data: dimensions and classifications. The Computer Journal, 41, 566-577.
• Hubert, L.J., Arabie, P., & Meulman, J.J. (1998). Graph-theoretic representation for proximity matrices through strongly-anti-Robinson or circular-anti-Robinson matrices. Psychometrika, 63, 341-358.
• Groenen, P.J.F., Commandeur, J.J.F., & Meulman J.J. (1998). Distance analysis of large data sets of categorical variables using object weights. British Journal of Mathematical and Statistical Psychology, 51, 217-232.
• Groenen, P.J.F., Heiser, W.J., & Meulman J.J., (1998). City-block scaling: smoothing strategies for avoiding local minima. In: I. Balderjahn, R. Mathar, & M. Schader, (Eds.), Classification, Data Analysis and Data Highways (pp. 46-53). Berlin: Springer Verlag.
• Bensmail, H. & Meulman, J.J., (1998). MCMC Inference for model-based cluster analysis. In: A. Rizzi, M. Vichi & H.H. Bock (Eds.), Advances in Data Science and Classification, (pp. 191-196). Berlin: Springer Verlag.
• Van der Ham, Th., Meulman, J.J., Van Strien, D.C. and Van Engeland, H. (1997). Empirically based subgrouping of eating disorders in adolescents: a longitudinal perspective. British Journal of Psychiatry, 170, 363-368.
• Van der Kooij, A.J., & Meulman J.J., (1997). MURALS: Multiple regression and optimal scaling using alternating least squares. In: W. Bandilla & F. Faulbaum (Eds.), Advances in Statistical Software, (pp. 99-106). Stuttgart: Lucius & Lucius. Stuttgart: Lucius & Lucius.
• Meulman, J.J. & Heiser, W.J., (1997). Graphical display of interaction in multiway contingency tables by use of homogeneity analysis: the 2 x 2 x 2 x 2 case. In: M. Greenacre & J. Blasius (Eds.), Visual Display of Categorical Data, pp. 277-296. New York: Academic Press.
• Rietveld, W.J., Boon, M.E. & Meulman, J.J. (1997). Seasonal fluctuations in the cervical smear detection rates for (pre)malignant changes and for infections. Diagnostic Cytopathology, 17, 452-455.
• Meulman, J.J. (1997). Multivariate data analysis through optimal scoring methods. In: Bulletin of the International Statistical Institute, Proceedings of the 51th Session of the ISI, (pp. 247-250). Istanbul: International Statistical Institute.
• Hubert, L.J., Arabie, P., Meulman, J.J. (1997). Hierarchical clustering and the construction of (optimal) ultrametrics using Lp - norms. In: Y. Dodge (Ed.), L1-Statistical Procedures and Related Topics, (pp. 457-472). Lecture Notes - Monograph Series, Hayward. CA: Institute of Mathematical Statistics.
• Hubert, L.J., Arabie, P., Meulman, J.J. (1997). The construction of globally optimal ordered partitions. In: B. Mirkin, F.R. McMorris, F.S. Roberts, and A. Rzhetsky (Eds.), Mathematical Hierarchies and Biology (pp. 299-311). DIMACS Series in Discrete Mathematics and Theoretical Computer Science. Providence, RI: American Mathematical Society.
• Heiser, W.J. & Meulman, J.J. (1997). Nonlinear Multivariate Analysis: Overview and recent advances. In: M. Ato Garcia & J. A. Lopez Pina (Eds.), IV Simposio de Metodologia de las Ciencias del Comportamiento (pp. 15-32). Murcia: Servicio de Publicaciones, Universidad.
• Heiser, W.J. & Meulman, J.J. (1997). Representation of binary multivariate data by graph models using the Hamming metric. In: E. Wegman and S. Azen (Eds.), Computing Science and Statistics, 29(2), (pp. 517-525). Fairfax, VA: Interface Foundation of North America, Inc.
• Groenen, P.J.F., Commandeur, J.J.F., & Meulman J.J. (1997). PIONEER: A program for distance-based multivariate analysis. In: W. Bandilla & F. Faulbaum (Eds.), Advances in Statistical Software, (pp. 83-90). Stuttgart: Lucius & Lucius.
• Meulman, J.J. (1996). Fitting a distance model to homogeneous subsets of variables: Points of view analysis of categorical data. Journal of Classification, 13, 249-266.
• Meulman, J.J. (1995) Book review of Elements of Dual Scaling: An Introduction to Practical Data Analysis, by Shizuhiko Nishisato, Journal of the American Statistical Association, 90, 395-396.
• Heiser, W.J. & Meulman, J.J. (1995). Nonlinear methods for the analysis of homogeneity and heterogeneity. In: W.J. Krzanowski (Ed.), Recent Advances in Descriptive Multivariate Analysis (pp. 51-89). Oxford: Oxford University Press.
• Heiser, W.J., & Meulman, J.J. (1994). Homogeneity analysis: exploring the distribution of variables and their nonlinear relationships. In: M. Greenacre, & J. Blasius (Eds.), Correspondence Analysis in the Social Sciences: Recent Developments and Applications (pp. 179-209). New York: Academic Press.
• Meulman, J.J., & Heiser, W.J. (1993). Nonlinear biplots for nonlinear mappings. In: O. Opitz, B. Lausen, & R. Klar (Eds), Information and Classification: Concepts, Methods and Applications (pp. 201-213). Berlin: Springer Verlag.
• Gower, J.C., & Meulman, J.J. (1993). The treatment of categorical information in physical anthropology. International Journal of Anthropology, 8, 43-51.
• Meulman, J.J. (1993). Principal coordinates analysis with optimal transformations of the variables: minimizing the sum of squares of the smallest eigenvalues. British Journal of Mathematical and Statistical Psychology, 46, 287-300.
• Meulman, J.J., & Verboon, P. (1993). Points of view analysis revisited: fitting multidimensional structures to optimal distance components with cluster restrictions on the variables. Psychometrika, 58, 7 – 35.
• Hillebrand, R., & Meulman, J.J. (1992). Afstand en nabijheid: verhoudingen in de Tweede Kamer (Distance and proximity: relations in the Second Chamber [of the Dutch Parliament]). In: J.J.A. Thomassen, M.P.C.M. Van Schendelen, and M.L. Zielonka-Goei (Eds.), De geachte afgevaardigde... hoe kamerleden denken over het Nederlandse parlement (pp. 98-128). Muiderberg: Coutinho.
• Meulman, J.J. (1992). The integration of multidimensional scaling and multivariate analysis with optimal transformations. Psychometrika, 57, 539-565.
• Meulman, J.J., Zeppa, P., Boon, M. E., & Rietveld, W.J. (1992). Prediction of various grades of cervical preneoplasia and neoplasia on plastic embedded cytobrush samples: discriminant analysis with qualitative and quantitative predictors. In: Analytical and Quantitative Cytology and Histology, 14, 60-72.
• Meulman, J.J. (1991). Principal components analysis using distances, including weights for variables and dimensions. In: S. Zidak (Ed), Proceedings of DIANA III, international meeting on discriminant analysis (pp. 178-196). Prague: Czechoslovak Academy of Sciences.
• Bolasco, S., Escoufier, Y., & Meulman, J.J. (1989). Symmetric and nonsymmetric approaches to the analysis of structured data matrices. In: R. Coppi and S. Bolasco (Eds), The analysis of multiway data matrices (pp. 215-220). Amsterdam: North-Holland.
• Meulman, J.J. (1989). Distance analysis in reduced canonical spaces. In: R. Coppi and S. Bolasco (Eds), The analysis of multiway data matrices (pp. 233-244). Amsterdam: North-Holland.
• Heiser, W.J., & Meulman, J.J. (1989), The approximation of K-subspaces by K other ones in a reduced common space. In: Bulletin of the International Statistical Institute, Proceedings of the 47th session of the International Statistical Institute (pp. 430-43). Paris: International Statistical Institute.
• Meulman, J.J., & Heiser, W.J. (1988). Second order regression and distance analysis. In: W. Gaul and M. Schader (Eds.), Data, expert knowledge and decisions (pp. 368-380). Berlin: Springer-Verlag.
• Meulman, J.J. (1988). OVERALS: Nonlinear generalized canonical analysis. In: A. Di Ciaccio and G. Bove (Eds.), Multiway '88 software guide (pp. 59-66). Rome: Dipartimento di Statistica, Probabilità e statistiche Applicate.
• Meulman, J.J. (1988). Nonlinear redundancy analysis via distances. In: H.H. Bock (Ed.), Classification and related methods of data analysis (pp. 515-522). Amsterdam: North-Holland Publishing Company.
• Heiser, W.J., & Meulman, J.J. (1987). Afstandsmodellen voor multivariate analyse (Distance models for multivariate analysis). In: H.F.M. Crombag, L.J.Th. van der Kamp and C.A.J. Vlek (Eds.) De Psychologie Voorbij: ontwikkelingen rond model, metriek en methode in de gedragswetenschappen (pp. 209-235). Lisse: Swets & Zeitlinger.
• De Leeuw, J., & Meulman, J.J. (1986). A special Jackknife for multidimensional scaling. Journal of Classification, 3, 97-112.
• De Leeuw, J., & Meulman, J.J. (1986). Principal components analysis and restricted multidimensional scaling. In: W. Gaul and M. Schader (Eds.), Classification as a tool of research (pp. 83-96). Amsterdam: North-Holland Publishing Company.
• Meulman, J.J., & Heiser, W.J. (1984). Constrained multidimensional scaling: more directions than dimensions. In: T. Havránek et al. (Eds.), COMPSTAT 1984: Proceedings in Computational Statistics (pp. 137-142). Vienna: Physica Verlag.
• Heiser, W.J., & Meulman, J.J. (1983). Constrained multidimensional scaling, including confirmation. Applied Psychological Measurement, 7, 381-404.
• Heiser, W.J., & Meulman, J.J. (1983). Analyzing rectangular tables by joint and constrained multidimensional scaling. Journal of Econometrics, 22, 139-167.

Computer Programs for Multivariate Data Analysis

• Busing, F. M. T. A, Heiser, W. J., Neufeglise, P., & Meulman, J. J. (2005). PREFSCAL. SPSS, Inc., Chicago.
• Van der Kooij, A.J., Neufeglise, P., & Meulman, J.J. (2004). MULTIPLE CORRESPONDENCE (revised and updated version of HOMALS). SPSS, Inc, Chicago.
• Busing, F. M. T. A, Heiser, W. J., Neufeglise, P., & Meulman, J. J. (1999). PROXSCAL. SPSS, Inc., Chicago.
• Van der Kooij, A.J., Neufeglise, P., & Meulman, J.J. (1999). CATPCA, Categorical Principal Components Analysis with Optimal Scaling. SPSS, Inc, Chicago.
• Groenen, P.J.F., Commandeur, J.J.F., Van Os, B.J., & Meulman, J.J. (1998),. PIONEER, Distance Based Multivariate Analysis, Data Theory Group, Leiden University.
• Van der Kooij, A.J., Neufeglise, P., & Meulman, J.J. (1997). CORRESPONDENCE, Correspondence Analysis and More General Biplots, including Constraints. SPSS, Inc, Chicago.
• Van der Kooij, A.J., Neufeglise, P., & Meulman, J.J. (1997). CATREG, Categorical Multiple Regression with Optimal Scaling. SPSS, Inc, Chicago.
• Groenen, P.J.F. & Meulman, J.J. (1995). DIAMOND, Distance Analysis of Multivariate Ordinal and Nominal Data. Department of Data Theory, Leiden University.
• Meulman, J.J. & Heiser, W.J. (1990). CATEGORIES. Optimal scaling programs for multivariate analysis (HOMALS, PRINCALS, OVERALS, ANACOR). SPSS Inc, Chicago.
• Meulman, J.J., Heiser, W.J., & Carroll, J.D. (1982). PREFMAP-3: Metric and nonmetric external unfolding using vector and ideal point models. Bell Telephone Laboratories, Murray Hill, NJ.
• Meulman. J.J. & Gifi, A. (1981). PRIMALS: One-dimensional homogeneity analysis, including principal components analysis with optimal scaling. Department of Data Theory, Leiden, 1981.

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Last updated: 4th June 2013