P.J.F. (Patrick) Groenen

Patrick J.F. Groenen is a professor of statistics at the Erasmus School of Economics (ESE).

Professor Groenen's work focuses on a range of issues relating to exploratory multivariate analysis and multidimensional scaling and their numerical algorithms.

He is the co-author of a textbook on multidimensional scaling published by Springer and has published articles in the top peer-reviewed journals including, among others, the Journal of Marketing Research, Computational Statistics and Data Analysis, Psychological Methods, Psychometrika, the Journal of Classification, the British Journal of Mathematical and Statistical Psychology, and the Journal of Empirical Finance.

• Key publications (13)
  • Beauchamp, J.P., Cesarini, D., Johannesson, M., Loos, M.J.H.M. van der, Koellinger, P.D., Groenen, P.J.F., Fowler, J.H., Rosenquist, J.N., Thurik, A.R. & Christakis, N.A. (2011). Molecular genetics and economics. Journal of Economic Perspectives, 25(4), 57-82.
  • Borg, I, Groenen, P.J.F., Jehn, K.A., Bilsky, W. & Schwartz, S.H. (2011). Embedding the organizational culture profile into Schwartz’s theory of universals in values. Journal of Personnel Psychology, 10(1), 1-12.
  • Heij, C., Dijk, D.J.C. van & Groenen, P.J.F. (2011). Forecasting with leading indicators by means of the principal covariate index. Journal of Business Cycle Measurement and Analysis, 4(1), 73-92.
  • Heij, C., Dijk, D.J.C. van & Groenen, P.J.F. (2011). Real-time macroeconomic forecasting with leading indicators: An empirical comparison. International Journal of Forecasting, 27(2), 466-481.
  • Loos, M.J.H.M. van der, Koellinger, P.D., Groenen, P.J.F., Rietveld, C.A., Rivadeneira, F., Rooij, F.J.A. van, Uitterlinden, A.G., Hofman, A. & Thurik, A.R. (2011). Candidate gene studies and the quest for the entrepreneurial gene. Small Business Economics, 37(3), 269-275.
  • Koellinger, P.D., Loos, M.J.H.M. van der, Groenen, P.J.F., Thurik, A.R., Rivadeneira, F., Rooij, F.J.A. van, Uitterlinden, A.G. & Hofman, A. (2010). Genome-wide assocation studies in economics and entrepreneurship research: promises and limitations. Small Business Economics, 35(1), 1-18.
  • Rosmalen, J.M. van, Herk, Hester van & Groenen, P.J.F. (2010). Identifying response styles: a latent-class bilinear multinomial logit model. Journal of Marketing Research, 47(1), 157-172.
  • Rosmalen, J.M. van, Koning, A.J. & Groenen, P.J.F. (2009). Optimal scaling of interaction effects in generalized linear modelling. Multivariate Behavioral Research, 44(1), 59-81.
  • Groenen, P.J.F., Nalbantov, G.I. & Bioch, J.C. (2008). SVM-Maj: a majorization approach to linear support vector machines with different hinge errors. Advances in Data Analysis and Classification, 2(1), 17-43.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2008). A graphical shopping interface based on product attributes. Decision Support Systems, 46(1), 265-276.
  • Busing, F.M.T.A., Groenen, P.J.F. & Heiser, W.J. (2005). Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation. Psychometrika, 70(1), 71-98.
  • Deun, K. van & Groenen, P.J.F. (2005). Majorization algorithms for inspecting circles, ellipses, squares, rectangles, and rhombi. Operations Research, 53(6), 957-967.
  • Borg, I & Groenen, P.J.F. (2005). Modern multidimensional scaling: Theory and applications (Springer Series in Statistics, 22). New York: Springer.

• Articles (72)
  • Tarakci, M., Ates, N.Y., Porck, J.P., van Knippenberg, D., Groenen, P.J.F. & Haas, M. de (2014). Strategic Consensus Mapping: A New Method for Testing and Visualizing Strategic Consensus Within and Between Teams. Strategic Management Journal.
  • Cecere, S, Groenen, P.J.F. & Lesaffre, E.M.E.H. (2013). The Interval-Censored Biplot. Journal of Computational and Graphical Statistics, 22(1), 123-134.
  • Cecere, S, Groenen, P.J.F. & Lesaffre, E.M.E.H. (2013). The Interval-Censored Biplot. Journal of Computational and Graphical Statistics, Accepted.
  • Exterkate, P., Dijk, D.J.C. van, Heij, C. & Groenen, P.J.F. (2013). Forecasting the yield curve in a data-rich environment using the factor-augmented Nelson-Siegel model. Journal of Forecasting, 32, 193-214.
  • Koellinger, P.D., Van der Loos, M., Rietveld, C.A., Benjamin, D.J., Cesarini, D., Eklund, N, Willems, S.M., Groenen, P.J.F., Uitterlinden, A.G., Hofman, A., Thurik, A.R. & Gentrepreneur Consortium, The (2013). GWA studies on entrepreneurship: The trade-off between phenotype accuracy and sample size. Behavior Genetics.
  • Loos, M.J.H.M. van der, Rietveld, C.A., Eklund, N, Koellinger, P.D., Rivadeneira Ramirez, F., Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, D.J., Biffar, R, Blankenberg, S, Boomsma, D.I., Cesarini, D., Cucca, F, Geus, E.J.C. de, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Hohne, B, Holle, R, Hottenga, J.J., Isaacs, A, Jarvelin, MR, Johannesson, M, Kaakinen, M, Kahonen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, L.J., Lehtimaki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, B.A., Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H., Schmidt, R., Senft, A., Smith, AV, Spector, T.D., Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, Duijn, C.M. van, Viikari, J, Volzke, H, Wichmann, HE, Wild, PS, Willems, S.M., Willemsen, G, Rooij, F.J.A. van, Groenen, P.J.F., Uitterlinden, A.G., Hofman, A. & Thurik, A.R. (2013). The Molecular Genetic Architecture of Self-Employment. PLoS One, 8(4).
  • Loos, M.J.H.M. van der, Haring, R, Rietveld, C.A., Baumeister, SE, Groenen, P.J.F., Hofman, A., Jong, F.H. de, Koellinger, P.D., Kohlmann, T., Nauck, M.A., Rivadeneira Ramirez, F., Uitterlinden, A.G., Rooij, F.J.A. van, Wallaschofski, H. & Thurik, A.R. (2013). Serum testosterone levels in males are not associated with entrepreneurial behavior in two independent observational studies. Physiology & Behavior, 119, 110-114.
  • Loos, M.J.H.M. van der, Rietveld, C.A., Eklund, N, Koellinger, P.D., Rivadeneira Ramirez, F., Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, D.J., Biffar, R, Blankenberg, S, Boomsma, D.I., Cesarini, D., Cucca, F, Geus, E.J.C. de, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Hoehne, B, Holle, R, Hottenga, J.J., Isaacs, A.J., Jarvelin, MR, Johannesson, M, Kaakinen, M, Kahonen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, L.J., Lehtimaki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, B.A., Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H., Schmidt, R., Senft, A., Smith, AV, Spector, T.D., Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, van Duijn, CM, Viikari, J, Voelzke, Henry, Wichmann, H-E., Wild, PS, Willems, S.M., Willemsen, G, Rooij, F.J.A. van, Groenen, P.J.F., Uitterlinden, A.G., Hofman, A. & Thurik, A.R. (2013). The Molecular Genetic Architecture of Self-Employment. PLoS One, 8(4), e60542.
  • Loos, M.J.H.M. van der, Haring, R, Rietveld, C.A., Baumeister, SE, Groenen, P.J.F., Hofman, A., Jong, F.H. de, Koellinger, P.D., Kohlmann, T., Nauck, M.A., Rivadeneira, F., Uitterlinden, A.G., Rooij, F.J.A. van, Wallaschofski, H. & Thurik, A.R. (2013). Serum testosterone levels in males are not associated with entrepreneurial behavior in two independent observational studies. Physiology & Behavior, 119, 110-114.
  • Rietveld, C.A., Medland, SE, Derringer, J, Yang, J., Esko, T, Martin, N.W., Westra, HJ, Shakhbazov, K, Abdellaoui, A, Agrawal, A., Albrecht, E, Alizadeh, B.Z., Amin, N., Barnard, J, Baumeister, SE, Benke, KS, Bielak, LF, Boatman, JA, Boyle, PA, Davies, G., Leeuw, C. de, Eklund, N, Evans, DS, Fehrmann, R, Fischer, K., Gieger, C, Gjessing, HK, Hägg, S, Harris, JR, Hayward, C, Holzapfel, C, Ibrahim-Verbaas, CA, Ingelsson, E, Jacobsson, B, Joshi, PK, Jugessur, A, Kaakinen, M, Kanoni, S, Karjalainen, J, Kolcic, I, Kristiansson, K, Kutalik, Z, Lahti, J, Lee, SH, Lin, P, Lind, PA, Liu, Y., Lohman, K, Loitfelder, M, McMahon, G, Marques Vidal, P, Meirelles, O, Milani, L, Myhre, R, Nuotio, ML, Oldmeadow, CJ, Petrovic, KE, Peyrot, WJ, Polasek, O, Quaye, L, Reinmaa, E, Rice, JP, Rizzi, TS, Schmidt, H., Schmidt, R., Smith, AV, Smith, JA, Tanaka, T, Terracciano, A, Loos, M.J.H.M. van der, Vitart, V, Völzke, H, Wellmann, J, Yu, L, Zhao, W, Allik, J, Attia, JR, Bandinelli, S, Bastardot, F, Beauchamp, J., Bennett, DA, Berger, K., Bierut, L, Boomsma, D.I., Bültmann, U, Campbell, H, Chabris, C.F., Cherkas, L, Chung, MK, Cucca, F, Andrade, M de, Jager, PL De, De Neve, J.E., Deary, IJ, Dedoussis, GV, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Elderson, MF, Eriksson, JG, Evans, DM, Faul, JD, Ferrucci, L, Garcia, ME, Grönberg, H, Gudnason, V, Hall, P., Harris, JM, Harris, T.B., Hastie, ND, Heath, AC, Hernandez, DG, Hoffmann, W., Hofman, W.H.A., Holle, R, Holliday, EG, Hottenga, J.J., Iacono, WG, Illig, T, Järvelin, MR, Kähönen, M, Kaprio, J, Kirkpatrick, RM, Kowgier, M, Latvala, A, Launer, L.J., Lawlor, DA, Lehtimäki, T, Li, J., Lichtenstein, P., Lichtner, P, Liewald, DC, Madden, PA, Magnusson, PKE, Mäkinen, TE, Masala, M, McGue, M, Metspalu, A, Mielck, A., Miller, MB, Montgomery, GW, Mukherjee, S., Nyholt, DR, Oostra, B.A., Palmer, LJ, Palotie, A, Penninx, BWJH, Perola, M, Peyser, PA, Preisig, M, Räikkönen, K, Raitakari, OT, Realo, A, Ring, SM, Ripatti, S, Rivadeneira, F., Rudan, I, Rustichini, A, Salomaa, V, Sarin, AP, Schlessinger, D, Scott, R.J., Snieder, H, St Pourcain, B, Starr, JM, Sul, JH, Surakka, I, Svento, R, Teumer, A, Lifelines Cohort Study, THE, Tiemeier, H., Rooij, F.J.A. van, Van Wagoner, DR, Vartiainen, E, Viikari, J, Vollenweider, P, Vonk, JM, Waeber, G, Weir, DR, Wichmann, H-E., Widen, E, Willemsen, G, Wilson, JF, Wright, AF, Conley, D., Davey-Smith, G, Franke, L, Groenen, P.J.F., Hofman, A., Johannesson, M, Kardia, SLR, Krueger, RF, Laibson, D., Martin, NG, Meyer, MN, Posthuma, D, Thurik, A.R., Timpson, NJ, Uitterlinden, A.G., Duijn, C.M. van, Visscher, P.M., Benjamin, D.J., Cesarini, D. & Koellinger, P.D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 240(6139), 1467-1471.
  • Sikorska, K., Rivadeneira, F., Groenen, P.J.F., Hofman, A., Uitterlinden, A.G., Eilers, P.H.C. & Lesaffre, E. (2013). Fast linear mixed model computations for genome-wide association studies with longitudinal data. Statistics in Medicine, 32(1), 165-180.
  • Velden, M. van de, Beuckelaer, A. de, Groenen, P.J.F. & Busing, F.M.T.A. (2013). Solving degeneracy and stability in nonmetric unfolding. Food Quality and Preference, 27(1), 85-95.
  • Ates, N.Y., Tarakci, M., Porck, J.P., van Knippenberg, D. & Groenen, P.J.F. (2012). How Middle Managers Get Subordinates on Board? The Moderating Role of Strategic Alignment with CEO. In ERIM Report Series.
  • Cecere, S, Leroy, R, Groenen, P.J.F., Lesaffre, E. & Declerck, D (2012). Estimating emergence sequences of permanent teeth in Flemish schoolchildren using interval-censored biplots: a graphical display of tooth emergence sequences. Community Dentistry and Oral Epidemiology, 40(suppl.1), 50-56.
  • Herk, Hester van, Groenen, P.J.F. & Rosmalen, J.M. van (2012). Waarden, segmenten en politieke partijen: Stabiliteit en verandering in de jaren nul. In K. Aarts & M. Wittenberg (Eds.), Nederland in de jaren nul (pp. 19-37). Amsterdam: Pallas, Amsterdam University Press.
  • Nalbantov, G.I., Groenen, P.J.F. & Smirnov, E. (2012). A Comparative Analysis of Instance-based Penalization Techniques for Classification. In H. Dai, J.N.K. Liu & E. Smirnov (Eds.), Reliable Knowledge Discovery (pp. 227-238). New York: Springer.
  • Porck, J.P., van Knippenberg, D., Tarakci, M., Ates, N.Y., Groenen, P.J.F. & de Haas, M. (2012). Strategic Consensus Between Groups: A Social Identity Perspective. In ERIM Report Series.
  • Sikorska, K., Rivadeneira, F., Groenen, P.J.F., Hofman, A., Uitterlinden, A.G., Eilers, P.H.C. & Lesaffre, E.M.E.H. (2012). Fast linear mixed model computations for genome-wide association studies with longitudinal data. Statistics in Medicine, 32(1), 165-180.
  • Beauchamp, J.P., Cesarini, D., Johannesson, M., Loos, M.J.H.M. van der, Koellinger, P.D., Groenen, P.J.F., Fowler, J.H., Rosenquist, J.N., Thurik, A.R. & Christakis, N.A. (2011). Molecular genetics and economics. Journal of Economic Perspectives, 25(4), 57-82.
  • Borg, I, Groenen, P.J.F., Jehn, K.A., Bilsky, W. & Schwartz, S.H. (2011). Embedding the organizational culture profile into Schwartz’s theory of universals in values. Journal of Personnel Psychology, 10(1), 1-12.
  • Heij, C., Dijk, D.J.C. van & Groenen, P.J.F. (2011). Forecasting with leading indicators by means of the principal covariate index. Journal of Business Cycle Measurement and Analysis, 4(1), 73-92.
  • Heij, C., Dijk, D.J.C. van & Groenen, P.J.F. (2011). Real-time macroeconomic forecasting with leading indicators: An empirical comparison. International Journal of Forecasting, 27(2), 466-481.
  • Loos, M.J.H.M. van der, Koellinger, P.D., Groenen, P.J.F., Rietveld, C.A., Rivadeneira, F., Rooij, F.J.A. van, Uitterlinden, A.G., Hofman, A. & Thurik, A.R. (2011). Candidate gene studies and the quest for the entrepreneurial gene. Small Business Economics, 37(3), 269-275.
  • Loos, M.J.H.M. van der, Groenen, P.J.F., Hofman, A., Koellinger, P.D., Rivadeneira, F., Rooij, F.J.A. van, Thurik, A.R. & Uitterlinden, A.G. (2011). De genetica van ondernemerschap. Economisch-Statistische Berichten, 96(4609s), 30-36.
  • Borg, I, Groenen, P.J.F., Jehn, K.A., Bilsky, W. & Schwartz, S.H. (2010). Embedding the organizational culture profile into Schwartz¿s theory of universals in values. Journal of Personnel Psychology, 10(1), 1-12.
  • Gower, J.C., Groenen, P.J.F. & Velden, M. van de (2010). Area biplots. Journal of Computational and Graphical Statistics, 19(1), 46-61.
  • Loos, M.J.H.M. van der, Koellinger, P.D., Groenen, P.J.F. & Thurik, A.R. (2010). Genome-wide association studies and the genetics of entrepreneurship. European Journal of Epidemiology, 25(1), 1-3.
  • Nalbantov, G.I., Franses, P.H.B.F., Groenen, P.J.F. & Bioch, J.C. (2010). Estimating the Market Share Attraction Model using Support Vector Regressions. Econometric Reviews, 29(5/6), 688-716.
  • Rosmalen, J.M. van, Herk, Hester van & Groenen, P.J.F. (2010). Identifying response styles: a latent-class bilinear multinomial logit model. Journal of Marketing Research, 47(1), 157-172.
  • Rosmalen, J.M. van, Koning, A.J. & Groenen, P.J.F. (2010). Optimaal schalen van interactie-effecten. In A.E. Bronner et al., P. Dekker, E. de Leeuw, L.J. Paas, K. de Ruyter, A. Smidts & J.E. Wieringa (Eds.), Ontwikkelingen in het marktonderzoek: Jaarboek 2010 (pp. 177-193). Haarlem: Spaarenhout.
  • Blasius, J., Greenarcre, M., Groenen, P.J.F. & Velden, M. van de (2009). Special issue on correspondence analysis and related methods. Computational Statistics & Data Analysis, 53, 3103-3106.
  • Rosmalen, J.M. van, Koning, A.J. & Groenen, P.J.F. (2009). Optimal scaling of interaction effects in generalized linear modelling. Multivariate Behavioral Research, 44(1), 59-81.
  • Rosmalen, J.M. van, Groenen, P.J.F., Trejos, J. & Castillo, W. (2009). Optimization strategies for two-mode partitioning. Journal of Classification, 26(2), 155-181.
  • Velden, M. van de, Groenen, P.J.F. & Poblome, J. (2009). Seriation by constrained correspondence analysis: A simulation study. Computational Statistics & Data Analysis, 53(8), 3129-3138.
  • Groenen, P.J.F., Nalbantov, G.I. & Bioch, J.C. (2008). SVM-Maj: a majorization approach to linear support vector machines with different hinge errors. Advances in Data Analysis and Classification, 2(1), 17-43.
  • Groenen, P.J.F., Hofman, A., Koellinger, P.D., Loos, M.J.H.M. van der, Rivadeneira, F., Rooij, F.J.A. van, Thurik, A.R. & Uitterlinden, A.G. (2008). Genome-wide association for loci influencing entrepreneurial behavior: The Rotterdam Study. Behavior Genetics, 38, 628-629.
  • Groenen, P.J.F. & Winsberg, S. (2008). 3WaySym-Scal: Three-way symbolic multidimensional scaling. In P. Brito, P. Betrand, G. Cucumel & F. de Carvalho (Eds.), Selected Contributions in Data Analysis and Classification (pp. 55-67). Berlin: Springer.
  • Heij, C., Dijk, D.J.C. van & Groenen, P.J.F. (2008). Macroeconomic forecasting with matched principal components. International Journal of Forecasting, 24(1), 87-100.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2008). A graphical shopping interface based on product attributes. Decision Support Systems, 46(1), 265-276.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2008). Choosing Attribute Weights for Item Dissimilarity using Clickstream Data with an Application to a Product Catalog Map. In Proceedings of the 2nd ACM International Conference on Recommender Systems (pp. 195-202). New York: ACM Press.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2008). An Online Shopping Interface Based on a Joint Product and Attribute Category Map. In Proceedings of 1st IUI Workshop on Recommendation and Collaboration ReColl 2008.
  • Geweke, J.F., Groenen, P.J.F., Paap, R. & Dijk, H.K. van (2007). Computational techniques for applied econometric analysis of macroeconomic and financial processes. Computational Statistics & Data Analysis, 51(7), 3506-3507.
  • Groenen, P.J.F., Nalbantov, G.I. & Bioch, J.C. (2007). Nonlinear support vector machines through iterative majorization and I-splines. In R. Decker & H-J. Lenz (Eds.), Advances in Data Analysis, Proceedings of the Geselschaft fur Klassification (pp. 149-162). Berlin: Springer.
  • Heij, C., Groenen, P.J.F. & Dijk, D.J.C. van (2007). Forecast comparison of principal component regression and principal covariate regression. Computational Statistics & Data Analysis, 51(7), 3612-3625.
  • Kagie, M., Groenen, P.J.F. & Wezel, M.C. van (2007). A graphical shopping interface based on product characteristics. In V. Oria, A. Elmagarmid, F. Lochovsky & Y. Saygin (Eds.), Proceedings of the 23rd International Conference on Data Engineering Workshops (pp. 791-800). Los Alamitos, CA: IEEE Computer Society.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2007). Online shopping using a two dimensional product map. In G. Psaila & R. Wagner (Eds.), E-Commerce and Web Technologies Vol. 4655. Lecture Notes in Computer Science (pp. 89-98). Berlin: Springer.
  • Kagie, M., Wezel, M.C. van & Groenen, P.J.F. (2007). A graphical shopping interface based on product attributes. In M. Somereen, S. Katrenko & P. Adriaans (Eds.), Proceedings of Benelearn 2007 (pp. 45-52). Amsterdam/Netherlands.
  • Linting, M., Meulman, J.J., Groenen, P.J.F. & Kooij, A.J. van der (2007). Nonlinear Principal Components Analysis: Introduction and Application. Psychological Methods, 12(3), 336-358.
  • Linting, M., Meulman, J.J., Groenen, P.J.F. & Kooij, A.J. van der (2007). Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap. Psychological Methods, 12(3), 359-379.
  • Barlow, J.L., Groenen, P.J.F., Park, H. & Zha, H. (2006). 2nd special issue on matrix computations and statistics. Computational Statistics & Data Analysis, 50(1), 1-4.
  • Franses, P.H.B.F., Groenen, P.J.F. & Wagelmans, A.P.M. (2006). Editorial introduction, special issue: 50 years of Econometric Institute and 60 years of Statistica Neerlandica. Statistica Neerlandica, 60(2), 79-79.
  • Groenen, P.J.F., Winsberg, S., Rodriguez, O. & Diday, E. (2006). I-scal: multidimensional scaling of interval dissimilarities. Computational Statistics & Data Analysis, 51, 360-378.
  • Groenen, P.J.F. & Ark, A. van (2006). Visions of 70 years of psychometrics: the past, present, and future. Statistica Neerlandica, 60(2), 135-144.
  • Groenen, P.J.F. & Winsburg, S. (2006). Multidimensional scaling of histogram dissimilarities. In V. Batagelj, H.-H. Bock, A. Ferligoj & A. Ziberna (Eds.), Data science and classification (pp. 161-170). Berlin: Springer.
  • Groenen, P.J.F. & Koning, A.J. (2006). A new model for visualizing interactions in analysis of variance. In M. Greenacre & J. Blasius (Eds.), Multiple correspondence analysis and related methods. (pp. 487-502). London: Chapman & Hall.
  • Kiers, H.A.L. & Groenen, P.J.F. (2006). Visualizing dependence of bootstrap confidence intervals for methods yielding spatial configurations. In S. Zani, A. Cerioli, M. Riani & M. Vichi (Eds.), Data analysis, classification and the forward search. (pp. 119-126). Berlin: Springer.
  • Nalbantov, G.I., Bioch, J.C. & Groenen, P.J.F. (2006). Solving and interpreting binary classification problems in marketing with SVMs. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nurnberger & W. Gaul (Eds.), From data and information analysis to knowledge engineering. (pp. 566-573).
  • Nalbantov, G.I., Bioch, J.C. & Groenen, P.J.F. (2006). Classification with support hyperplanes. In J. Furnkranz, T. Scheffer & M. Spiliopoulou (Eds.), MACHINE LEARNING: ECML 2006, PROCEEDINGS Lecture Notes in Computer Science (pp. 703-710). Berlin/Heidelberg: Springer.
  • Busing, F.M.T.A., Groenen, P.J.F. & Heiser, W.J. (2005). Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation. Psychometrika, 70(1), 71-98.
  • Deun, K. van & Groenen, P.J.F. (2005). Majorization algorithms for inspecting circles, ellipses, squares, rectangles, and rhombi. Operations Research, 53(6), 957-967.
  • Deun, K. van, Groenen, P.J.F., Heiser, W.J., Busing, F.M.T.A. & Delbeke, L. (2005). Interpreting degenerate solutions in unfolding by use of the vector model and the compensatory distance model. Psychometrika, 70(1), 45-69.
  • Groenen, P.J.F. & Velden, M. van de (2005). Multidimensional Scaling. In B.S. Everitt & D.C. Howel (Eds.), Encyclopedia of Statistics in Behavioral Sciences Vol. 2. Encyclopedia of Statistics in Behavioral Sciences (pp. 1280-1289).
  • Groenen, P.J.F., Giaquinto, P. & Kiers, H.A.L. (2005). An improved majorization algorithm for robust procrustes analysis. In M. Vichi, P. Monari, S. Mignani & A. Montanari (Eds.), New Delelopments in Classification and Data Analysis New Developments in Classification and Data Analysis (pp. 151-158). Bologna: Springer-Verlag.
  • Groenen, P.J.F. & Velden, M. van de (2004). Inverse correspondence analysis. Linear Algebra and its Applications, 388, 221-238.
  • Groenen, P.J.F. & Meulman, J.J. (2004). A comparison of the ratio of variances in distance-based and classical multivariate analysis. Statistica Neerlandica, 58(4), 428-439.
  • Groenen, P.J.F. (2004). Visualisatie met dynamische meerdimensionele schaling. Ontwikkelingen in het Marktonderzoek / Jaarboek MOA, 2004, 183-195.
  • Pietersz, R. & Groenen, P.J.F. (2004). Rank reduction of correlation matrices by majorization. Quantitative Finance, 4(6), 649-662.
  • Velden, M. van de, Groenen, P.J.F. & Poblome, J. (2003). Seriation met bedingter Korrespondenzanalyse: Simulationsexperimente. Archaologische informationen, 26, 449-455.
  • Poblome, J. & Groenen, P.J.F. (2002). Constrained correspondence analysis for seriation of Sagalassos tablewares. In M. Doerr & A. Sarris (Eds.), Proceedings of the 30th Conference : Computer Applications and Quantitative Methods in Archaeology (pp. 90-97). Heraklion, Crete: CAA 2002.
  • Groenen, P.J.F. & Jajuga, K. (2001). Fuzzy clustering with squared Minkowski distances. Fuzzy Sets & Systems, 120(2), 227-237.
  • Groenen, P.J.F. & Poblome, J. (2001). Constrained correspondence analysis for seriation in archaeology applied to Sagalassos ceramic tablewares. In M. Schwaiger & O. Opitz (Eds.), Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 16-16, 2001 - Exploratory Data Analysis in Empirical Research (pp. 301-306). Berlin/ Heidelberg/ New York: Springer-Verlag.
  • Groenen, P.J.F. & Franses, P.H.B.F. (2000). Visualizing time-varying correlations across stock market. Journal of Empirical Finance, 7(2), 155-172.

• Books (3)
  • Borg, I, Groenen, P.J.F. & Mair, P. (2013). Applied Multidimensional Scaling (SpringerBriefs in Statistics). Heidelberg: Springer.
  • Borg, I, Groenen, P.J.F. & Mair, P. (2010). Multidimensionele Skaliering (Sozialwissenschaftliche Forschungsmethoden, Band 1). Muenchen: Rainer Hampp Verlag.
  • Borg, I & Groenen, P.J.F. (2005). Modern multidimensional scaling: Theory and applications (Springer Series in Statistics, 22). New York: Springer.

• Book contributions (2)
  • Groenen, P.J.F., Kaymak, U. & Rosmalen, J.M. van (2007). Fuzzy clustering with Minkowski distance functions. In J Valente de Oliveira & W Pedrycz (Eds.), Advances in fuzzy clustering and its applications (pp. 53-68). Chicester: Wiley.
  • Groenen, P.J.F. & Koning, A.J. (2005). Generalized bi-additive modelling for categorical data. In M. Vichi, P. Monari, S. Mignani & M. Montanari (Eds.), New development in classification and data analysis (pp. 159-166). Heidelberg: Springer.

• Professional publications (3)
  • Exterkate, P., Dijk, D.J.C. van, Heij, C. & Groenen, P.J.F. (2013). Forecasting the yield curve in a data-rich environment using the factor-augmented Nelson-Siegel model. (EI reprint serieEI-1609 ). Rotterdam: Econometric Institute.
  • Pietersz, R. & Groenen, P.J.F. (2004). A major Libor fit. Risk. Currencies, Interest Rates, Equities, 17(12), 102.
  • Groenen, P.J.F. & Stappers, J. (2003). Dynamische meerdimensionele schaling. Stator, 4(2), 4-10.

• Editorial position (4)

  Journal	Advances in Data Analysis and Classification
  Role	Associate Editor
  Start date	01-01-2007
  Journal	Psychometrika
  Role	Associate Editor
  Start date	01-01-1997
  Journal	Statistica Neerlandica
  Role	Associate Editor
  Start date	01-01-2002
  Role	Associate Editor
  Start date	01-01-2002
  End date	01-04-2009

2013/2014	Advanced Statistical Methods
Code:	BERM.AMC.002
ECTS:	5
Course level:	Advanced Methodology
2013/2014	Scientific Integrity
Code:	BERM.SKL.009
ECTS:	1
Course level:	Skill
2013/2014	Statistical Methods
Code:	BERM.MC.004
ECTS:	6
Course level:	Methodology
2012/2013	Advanced Statistical Methods
Code:	BERM.AMC.002
ECTS:	5
Course level:	Advanced Methodology
2012/2013	Scientific Integrity
Code:	BERM.SKL.009
ECTS:	1
Course level:	Skill
2012/2013	Statistical Methods
Code:	BERM.MC.004
ECTS:	6
Course level:	Methodology

Jun 10, 2009	Joint ERIM-Unipark Symposium on Online Research: Insights Into Methods and their Application in Practice
	ERIM Institute Event |
Apr 23, 2009	Efficient Experimental Designs for Choice-Based Conjoint Analysis
	ERIM Research Seminar | Marketing
Apr 21, 2009	Modeling Geo-dependent Attitudes Using Bayesian Spatial Factor Analysis
	ERIM Research Seminar | Marketing
Sep 12, 2008	Introduction and Developments on Support Vector Machines and Classification
	ERIM Research Workshop |
Jun 25, 2007	CARME 2007 Conference
	Conference | Marketing
Mar 21, 2005	Visualizing Consumers' Preference for Products by Ordinal Unfolding using PrefScal
	ERIM Research Seminar | Marketing

Genetics and economic behavior

Several PhD positions for genoeconomics are available as part of a joint research initiative of Erasmus University Rotterdam and the University of Amsterdam. Genoeconomics is a newly emerging research field that investigates the molecular genetic architecture of economic preferences (e.g. risk preferences, time discounting) and important lifetime outcomes (e.g. educational attainment, happiness, self-employment). This research could be transformative for the social sciences by providing new tools and insights to study the causes of behaviors and outcomes. Furthermore, the results of this research can inform medical research by identifying relevant causal pathways for disease outcomes (e.g. cognition-related or cardio-vascular diseases) that can help to identify individuals at risk early on. The PhD candidates will be embedded in an international, interdisciplinary research team that spearheads this new research field.

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Economic behaviour, in particular entrepreneurship, and psychological markers

Little is known about the relation between psychological markers (i.e., validated psychiatric symptom scores) such as attention deficit and hyperactivity (ADHD) symptoms and economic, non-clinical, behaviour like entrepreneurship. However, there are some early indications that individuals with high levels of ADHD have higher entrepreneurship intentions when compared to other individuals and that entrepreneurs with high levels of ADHD have a higher level of entrepreneurial orientation than entrepreneurs with a low ADHD level. The present project takes entrepreneurship as a starting point to investigate the relation between psychological markers and economic behaviour. Knowledge of this relation could advance the field of occupational choice as well as that of psychology, and physical and mental well-being (happiness). A mismatch between the real occupational choice and the one predicted by a psychological profile may be detrimental to one’s mental and physical well-being (happiness). The research will focus on entrepreneurship but will not be confined to it: other manifestations of economic behaviour, in particular occupational choice, such as unemployment or education may follow. The research will try to identify the relation between multiple psychological markers for psychopathologies (for example: ADHD, addictions, narcissism, dyslexia) and entrepreneurship. In this research project, neuroscience techniques such as electroencephalography (EEG) are employed to study their association with these psychological markers. The project is a joint research initiative of Erasmus School of Economics and the Faculty of Social Sciences (Psychology). The PhD position will be affiliated with the newly established Erasmus University Rotterdam Institute of Biology and Economic Behaviour (EURIBEB). There will also be close collaboration with the newly established Erasmus Economics of Happiness research Organisation (EHERO). Furthermore, the PhD candidate will be part of an international research team that spearheads this new research field. Finally, the Erasmus University Behavioural Lab offers excellent facilities to conduct these cutting-edge studies, and is internationally accredited for its service.

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When do we agree on company strategy?: Determinants of strategic consensus

Organizational strategy can be a key component in organizational success: strategy can inspire, give direction, and facilitate coordination and cooperation. Yet, the reality of many organizations is that even though they formulate a strategy at the top management level, this strategy is hardly known or understood throughout the organization. An organizational strategy can only be as good as its implementation in employee actions, and a key issue in firm performance thus is the extent to which there is a shared understanding of the organization’s strategy. This project is aimed at studying the determinants of such so-called strategic consensus. In doing so the project focuses on such behavioral and organizational determinants of strategic consensus as leadership, social networks, and team processes. Its ultimate aim is to produce knowledge about the causes and consequences of strategic consensus that will allow companies to better implement their strategy.

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Robustifying the analysis of Likert-scale data

In many areas in economics and management such as marketing, strategy and entrepreneurship, organizational behavior, etc., questionnaires are at the core of the data collection. Often, the answers in the questionnaires are given on a discrete scale, a so-called Likert scale. Outliers (i.e., individuals that behave differently from the majority) are a common problem in such data. For instance, somebody may not take the questionnaire seriously and give random answers, a respondent might use a reversed scale by mistake, or it may simply be the case that a small minority responds differently from the majority. For reliable statistical analysis, methods that are able to identify potential outliers and downweight those observations are therefore necessary. In the existing literature on robust statistics, however, the main concern is usually to prevent outliers from causing arbitrarily large deviations from the true model parameters, while a certain bias is often accepted as a trade-off. With Likert scale data, on the other hand, arbitrarily large deviations from the true parameters are not possible due to the bounded nature of the scale. Instead, robust methods should be designed to be able to detect even small deviations from the majority of the data and to have minimal bias in the presence of outliers. Hence the aim of this project is to investigate the behavior of existing robust methods for Likert scale data, and to use this knowledge to develop new methods that take into account the nature of Likert scale data and typical outlier scenarios.

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